Citation: Sherazi, H.H.R.; Zorbas, D.;

O’Flynn, B. A Comprehensive Survey

on RF Energy Harvesting:

Applications and Performance

Determinants. Sensors 2022, 22, 2990.

https://doi.org/10.3390/s22082990

Academic Editors: Ergin Dinc and

Oktay Cetinkaya

Received: 27 February 2022

Accepted: 8 April 2022

Published: 13 April 2022

Publisher’s Note: MDPI stays neutral

with regard to jurisdictional claims in

published maps and institutional affil-

iations.

Copyright: © 2022 by the authors.

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license (https://

creativecommons.org/licenses/by/

4.0/).

sensors

Review

A Comprehensive Survey on RF Energy Harvesting:Applications and Performance Determinants

Hafiz Husnain Raza Sherazi 1,2 , Dimitrios Zorbas 2,3,* and Brendan O’Flynn 2

1 School of Computing and Engineering, University of West London, London W5 5RF, UK; sherazi@uwl.ac.uk2 Tyndall National Institute, University College Cork, T12R5CP Cork, Ireland; brendan.oflynn@tyndall.ie3 Department of Computer Science, School of Engineering & Digital Sciences, Nazarbayev University,

Nur-Sultan 010000, Kazakhstan* Correspondence: dimzorbas@ieee.org

Abstract: There has been an explosion in research focused on Internet of Things (IoT) devices inrecent years, with a broad range of use cases in different domains ranging from industrial automationto business analytics. Being battery-powered, these small devices are expected to last for extendedperiods (i.e., in some instances up to tens of years) to ensure network longevity and data streams withthe required temporal and spatial granularity. It becomes even more critical when IoT devices areinstalled within a harsh environment where battery replacement/charging is both costly and labourintensive. Recent developments in the energy harvesting paradigm have significantly contributedtowards mitigating this critical energy issue by incorporating the renewable energy potentiallyavailable within any environment in which a sensor network is deployed. Radio Frequency (RF)energy harvesting is one of the promising approaches being investigated in the research communityto address this challenge, conducted by harvesting energy from the incident radio waves from bothambient and dedicated radio sources. A limited number of studies are available covering the stateof the art related to specific research topics in this space, but there is a gap in the consolidation ofdomain knowledge associated with the factors influencing the performance of RF power harvestingsystems. Moreover, a number of topics and research challenges affecting the performance of RFharvesting systems are still unreported, which deserve special attention. To this end, this article startsby providing an overview of the different application domains of RF power harvesting outliningtheir performance requirements and summarizing the RF power harvesting techniques with theirassociated power densities. It then comprehensively surveys the available literature on the horizonsthat affect the performance of RF energy harvesting, taking into account the evaluation metrics,power propagation models, rectenna architectures, and MAC protocols for RF energy harvesting.Finally, it summarizes the available literature associated with RF powered networks and highlightsthe limitations, challenges, and future research directions by synthesizing the research efforts in thefield of RF energy harvesting to progress research in this area.

Keywords: energy harvesting; RF powered wireless networks; RF-harvesting techniques; energypropagation models; RF circuit design; MAC protocols for RF power harvesting

1. Introduction

The IoT [1] is a research area that has been attracting the attention of research commu-nities around the globe for the past decade. The IoT can generally be seen as a networkedsystem of smart interconnecting objects (i.e., sensors, actuators, machines, smart phones,tablets, etc.). These devices have the ability to sense different types of data (i.e., temperature,pressure, light, acceleration, and so on) from the environment, process it in real-time, andtransmit it to the cloud for analysis in order to react to some events of interest. As per somerecent statistics forecast by industrial leaders, approximately 125 billion connecting devicesare expected to be deployed by 2030 as a part of the IoT, which represents more than sixteentimes the world’s population [2]. It is estimated that the IoT market will be worth in the

Sensors 2022, 22, 2990. https://doi.org/10.3390/s22082990 https://www.mdpi.com/journal/sensors

Sensors 2022, 22, 2990 2 of 36

region of USD 1.1 Trillion by the year 2023 and will enable industry activity in this areato rapidly scale up their business operations and associated Research and Developmentactivities [2]. This massive explosion of smart devices will not only pave the way towardsubiquitous services in different application domains (i.e., Industry 4.0, smart home, smartagriculture, smart supply chain, and smart city, to name a few) [3], but will also open upresearch activities addressing associated research challenges (e.g., energy efficiency, longradio coverage, optimum throughout and latency, to name a few) in all these domains [4–8].IoT devices are, in general, battery-powered in nature and the energy-intensive systemoperation being carried out in the aforementioned use cases requires that the batteries berecharged/replaced after a certain period, which is expensive in terms of both cost andlabour [9]. Moreover, these batteries are also a source of carbon emission with adverseenvironmental effects [10]. Thanks to the potential of renewable energy sources [11,12]available within the specific deployment environment, the surplus harvested energy avail-ability could circumvent power issues in smart devices to significantly prolong the networklifetime to make them ‘deploy and forget’. Table 1 summarizes different renewable energysources along with their harvesting potential and conversion efficiencies [13].

Table 1. Renewable energy sources along with their power densities and efficiency.

Harvesting Method Power Density Efficiency

Solar energy—outdoors 15 mW/cm3-bright sunny day 10–25%0.15 mW/cm3-cloudy day

Solar energy—indoors 100 µW/cm3

Vibrations (piezoelectric—shoe inserts) 330 µW/cm3-105 Hz 25–50%Vibrations (electrostatic conversion) 184 µW/cm3-10 HzVibrations (electromagnetic conversion) 0.21 mW/cm3-12 HzThermoelectric (5–20 °C gradient) 40 µW-10 mW/cm3 0.1–3%Magnetic field energy 130 µW/cm3-200 µT, 60 Hz 30–74.4%Wind energy 65.2 µW/cm3-5 m/s 20–40%RF energy 0.08 nW-1 µW/cm2 30–88%

RF energy harvesting, is one of a number of renewable energy sources which canprovide an alternative to batteries as a power source for wireless sensors. RF energy refersto the amount of energy harvested from the RF signals (i.e., both ambient and dedicated)emitted by a transmitter and received by the harvesting circuit to generate an electricalenergy as shown in Figure 1. RF energy harvesting offers a promising solution for energyconstrained networks (such as the IoT) by converting RF signals into electrical energy whichcan be used to provision the energy needs of the smart devices in order to prolong theirlifetime. In a typical RF energy harvesting use case, a radio signal with a frequency rangebetween 3 kHz and 3 GHz could be employed as a medium to provide the electromagneticenergy from the transmitter source to the receiver [14].

Figure 1. RF energy harvesting architecture.

RF energy harvesting as an energy transfer mechanism can provide a comparativelysustainable supply with easy availability, when compared to other energy harvesting meth-ods (such as vibration, solar, or thermal), and can help meet the energy requirements ofIoT-oriented energy-constrained networks. This can help meet the energy requirements

Sensors 2022, 22, 2990 3 of 36

associated with communication primitives, sensing, processing, and transmission opera-tions. Hence, such RF energy harvesting has numerous applications in several domainsand as a result several manufacturers are developing commercial solutions to addressthis market need. Some examples are the TX91501b Powercaster transmitter [15], thePowerspot [16], and the Cota system [17,18]. A number of experimental evaluations ofRF energy harvesting systems with a variety of harvesting sources are summarized inTable 2, where it is clearly evident that the levels of energy generated significantly variesover the different distances and the strength of the source RF signals, which is typicallyin the order of µW which is generally sufficient to power up various low-power IoT devices.

Methodology: This article aims to address three main objectives. First, we provide anoverview of the applications of RF power harvesting with their performance requirementsand develop an understanding of the different harvesting techniques along with their powerdensities to familiarize the reader with the baseline performance requirements and achiev-able power densities by employing different RF harvesting techniques (Sections 2 and 3).Second, we provide an analysis on the state of the art covering the most critical areas thataffect the performance of RF energy harvesting systems and how the recent developmentshave contributed towards performance optimization focusing on the best design considera-tions, innovative techniques, and novel architectures (Sections 4–7). Third, we highlight anumber of limitations and research challenges to be addressed so as to obtain the optimumperformance levels for IoT devices and to pave the future directions with research potentialfor the new entrants in this domain (Section 8).

The growing interest in RF energy harvesting, being a promising renewable energysolution, has also identified a number of technical challenges associated with the energyharvesting process (see Section 8.1), including antenna design for transmitters and receivers,rectifiers, and matching networks. There are a limited number of studies presently available,aimed at reviewing the current state of the art in the literature on RF energy harvesting(as shown in Table 3), but these studies do not give a clear view of the performancedeterminants for RF power harvesting systems. In general, the reviews are focused onspecific aspects of the RF energy harvesting domain (such as hardware design issues [19,20],RF powered wireless networks [21], circuit and protocol design [22], harvesting sources [23],trade-offs and design methodologies [24,25], and conceptualization [26]). Moreover, manyof the surveys available in the area are quite old and the most recent contributions areconsequently unreported for the researchers currently working in this popular area. To thebest of the authors’ knowledge, no comprehensive study is currently available highlightingthe critical areas that directly influence the performance of RF energy harvesting systems.

Table 2. Experimental evaluations for RF power harvesting.

Transmitter Transmit Power Frequency Distance Harvesting Power(at a Reference Distance)

Isotropic RF transmitter [27] 4 W 902–928 MHz 15 m 5.5 µWIsotropic RF transmitter [28] 1.78 W 868 MHz 25 m 2.3 µWIsotropic RF transmitter [15] 1.78 W 868 MHz 27 m 2 µWTX91501 powercaster transmitter [29] 3 W 915 MHz 5 m 189 µWTX91501 powercaster transmitter [29] 3 W 915 MHz 11 m 1 µWKing TV tower [30] 960 kW 674–680 MHz 4.1 km 60 µW

Sensors 2022, 22, 2990 4 of 36

Table 3. A summary of the state of the art surveys on RF power harvesting.

Reference Focus Objectives Year

Verma et al. [19] Hardware design issues

– Surveys the literature on the theoretical stud-ies of RF harvesting.

– Outlines the possible energy harvesting andenergy transfer technologies.

2016

Mukminin et al. [20] RF energy sources

– Studies the literature on the use of RF energyharvesting.

– Outlines a range of antennas suitable for RFenergy harvesting.

2020

Lu et al. [21] RF powered wireless networks

– Overviews the RF energy harvesting networkarchitecture.

– Presents circuit design background.– Explores design issues in the development of

RF harvesting networks.

2015

Nintanavongsan et al. [22] Circuit and protocol design

– Studies the fundamental design of RF energyharvesting circuits and protocols.

– Discusses the impact of energy replenishmentcapabilities on circuits and protocols.

2014

Sidhu et al. [23] energy harvesting sources

– Surveys a range of ambient RF enegry har-vesting sources.

– Reviews the progress of ambient sources thatis useful in designing the RF energy harvest-ing model.

2019

Soyata et al. [24]Trade-offs and methodologieson RF harvesting for embed-ded systems

– Overviews the passive RF energy receptionand harvesting circuits.

– Discusses RF energy harvesting in the contextof embedded systems.

– Analysis different combinations of systemcomponents.

2016

Tran et al. [25] Design methodologies and ap-plications for RF harvesting

– Summarizes RF power harvesting technolo-gies to design the system.

– Provides discussion on different design alter-natives and their trade-offs for RF power har-vesting.

2017

Srininvasu et al. [26] Conceptualization of RF en-ergy Harvesting

– Studies the optimal rectenna architecture formaximizing power conversion efficiency.

– Discusses system architecture for RF harvest-ing networks and applications.

2019

Sensors 2022, 22, 2990 5 of 36

Table 3. Cont.

Reference Focus Objectives Year

Clerckx et al. [31]Future Networks With Wire-less Power Transfer and EnergyHarvesting

– Presents the recent theory, designs, proto-types, and experiments in the area [32–34].

– Shows how metamaterials and metasurfacessuch as intelligent reflecting surfaces can sig-nificantly improve the power transfer effi-ciency and operational distance [35,36].

– Use of backscatter communications for RFpower harvesting systems [37,38].

– Resource allocation and safety constraintsfor Simultaneous Wireless Information andPower Transfer [39,40].

– A review on fundamental principles of pri-mary PHY attacks, covering jamming, eaves-dropping, and detection of covert [41].

2022

This SurveyApplication domains and per-formance determinants of RFpowered systems

– Overviews a range of applications of RF har-vesting along with their performance require-ments.

– Studies the factors affecting the performanceof RF harvesting systems.

– Highlights challenges and future research di-rections critical to system design and perfor-mance optimization.

2022

Contributions: The present study achieves the following four-fold contributions:

• We overview a range of application domains benefiting from RF energy harvesting toachieve an optimum performance level, where we outline a number of performancerequirements with respect to each of the application domains, in order to familiarizethe readers with the baseline performance requirements that are expected to be metby RF energy harvesting systems.

• We investigate different RF power harvesting techniques along with their average andpeak power densities in order to assess their suitability and limitations for a numberof use cases in different application domains.

• We thoroughly study the performance determinants for RF power harvesting systemsthat critically influence the performance of these systems. This includes the evalua-tion metrics for RF energy harvesting and energy propagation models that affect theperformance of RF-based systems. In addition, there is an analysis of rectenna archi-tectures and the power conversion efficiency associated with different rectenna designapproaches, and medium access protocols to support energy exhaustive operation inRF powered networks.

• We highlight the open issues, challenges, and lessons learned from recent implemen-tations, and future research directions by synthesizing the research efforts that havebeen put in place in the field of RF energy harvesting in recent years.

This study aims at providing a clear road map for researchers to help them identify therelevant design considerations and research challenges that are crucial to the performanceof RF energy harvesting systems.

Organization: The remainder of this article is organized as follows: Section 2 overviewsthe prospective applications of RF power harvesting along with their performance require-ments. The RF harvesting techniques widely adopted by the industry along with theiraverage and peak power densities are investigated in Section 3. The evaluation metrics forRF energy harvesting systems that effectively evaluate the performance of these systemsare summarized in Section 4. Similarly, Section 5 presents the energy propagation models

Sensors 2022, 22, 2990 6 of 36

that affect the performance of RF based systems. Section 6 surveys different rectenna archi-tectures, covering an in-depth analysis on the power conversion efficiency with differentrectenna design approaches being one of the key performance criteria. Moreover, Section 7presents an overview of MAC protocols specifically designed for RF energy harvestingsystems. Section 8 highlights the open issues, challenges, and lessons learned from recentimplementations, and future research directions by synthesizing the research efforts in thefield of RF energy harvesting. Finally, concluding remarks are provided in Section 9.

2. Applications of RF Energy Harvesting

RF energy harvesting has plays a role in a number of application domains to achieveenergy-efficient operation for a range of use cases. According to a report [42], energysustainability and integrated services for automation are some of the main driving factorsfor a majority of applications in different domains. This section aims at reviewing thestate of the art specifically targeting the most prominent applications where RF energyharvesting systems have been designed to meet a range of performance requirements (asoutlined in Table 4) to achieve a unique set of objectives for the following applications.

Table 4. Performance requirements for different application domains.

Application Domain Coverage TransmissionFrequency

OperationalCost

EnergyEfficiency Latency Network

Type

Internet of Things Varies * Medium Medium Varies * Medium WAN

Industrial Automation Medium High High Low Medium WAN

Healthcare Informatics Low Low Low High Low PAN

Radio Frequency Identifi-cation Low Low Low High Low PAN

Smart Buildings/StructuralHealth Monitoring Medium Low High Low WAN

* Varies from low to high depending on the type of IoT use cases. PAN stands for Personal Area Networks, WANstands for Wide Area Networks.

2.1. Ubiquitous Internet of Things

The Internet of Things [1] is paving the way forward towards ubiquitous servicesin different application domains. This technological advancement simultaneously givesbirth to a new set of challenges towards achieving energy optimal IoT operation [43].Choudhary et al. [44] emphasize the need for power harvesting in a range of IoT appli-cations and outline a number of energy harvesting technologies, methodologies, andarchitectures. Their work further surveys different energy harvesting sources and tech-niques for the researchers working in this domain. Ref. [45] is another attempt towardspresenting a clear vision of green IoT, where IoT nodes are designed for extremely lowenergy consumption and are fed through renewable energy solutions. The authors aimto describe the current state of the art and provide insights into energy-saving practicesand strategies for green IoT. The major contribution of this publication is the review anddiscussion of the substantial issues enabling hardware green IoT to focus on green wirelesssensor networks and green radio-frequency identification. In Ref. [46], the authors advocatethat ambient energy harvesting from the environment could be the only viable option forprolonging the lifetime of large scale interconnected networks.

2.2. Industrial Automation

Being a key enabler in the evolution of Industry 4.0, energy is a vital component inthe integration of computational processes with the physical processes in a smart pro-duction environment. In such an environment, a variety of smart devices are deployed

Sensors 2022, 22, 2990 7 of 36

for the monitoring of physical industrial processes and these are continuously engagedin energy exhaustive operations. For example, the frequent sensing and transmissionof important data of interest to the expert system is critical to Cyber-Physical Systems(CPS) [47]. However, this is known to be a power-hungry operation for these deviceswhich are primarily battery-powered in nature [48]. Hence, the critical aspect of these lowpower devices is the energy efficient operation and optimization of energy managementand harvesting mechanisms.

Tahir et al. [49] present a low-cost scheme for measuring the RF signals on theproduction floor with the aim of optimizing the manufacturing operation within a smartindustry. The RF energy can then be harvested at several locations of the manufacturingline and the harvested energy supplied to Micro-Electro-Mechanical Systems (MEMS) andsensors installed on the production line. As such the RF harvesting system can reduce themaintenance labor and operational costs associated with frequent battery replenishment.Similarly, Tang et al. [50] outline a range of energy harvesting technologies suitable forindustrial automation and investigate the energy consumption of small sensor devicesdeployed across the production floor. The authors evaluate the energy harvesting potentialthat can be harnessed from the industrial processes. Finally, a number of energy harvestingtechnologies and prototypes are reviewed in different areas with a special emphasis ontheir use for production machinery in industrial applications.

Similarly, a collection of tiny nodes capable of sensing the environment, performingsimple computations and supporting wireless communications to accomplish a monitoringtask can be referred to as Wireless Sensor Networks (WSNs). Since their emergence anumber of decades ago, WSNs have been adopted in a variety of use cases including, butnot limited, to Smart Homes [51], Smart Healthcare Systems [52], Intelligent TransportationSystems [53], Disaster Management Systems [54], and Continuous Video SurveillanceSystems [55]. The energy consumption of such wireless sensor nodes varies depending onthe use case requirements. To ensure a sustainable system design, RF power harvestingcapabilities can be incorporated in to the WSN system design. To this end, Ref. [56]presents a practical scheme for RF energy harvesting and management for WSNs basedon an Improved Energy Efficient Ant Based Routing Algorithm (IEEABR) and taking intoaccount the energy consumption of the sensor nodes and availability of RF energy. Theauthors also cover the measurement statistics of RF power density, estimation of receivedpower, storage of harvested power to a storage media, and associated management of RFpower harvesting in the context of WSNs.

A similar work [57] outlines the requirements and fundamental principles for power-ing up WSNs by RF energy harvesting. The harvesting feasibility and the power density isevaluated to conclude that RF energy harvesting is most appropriate to feed small-sizedsensors. Similarly, Ref. [58] overviews the magnitudes of various ambient energy harvest-ing sources for WSNs. They argue that 10–100 µW, although small, is an ample order ofmagnitude for a range of applications in WSNs. Meanwhile, Ref. [59] is another workto implement RF energy harvester in Mica2 nodes with an adopted duty cycle to harvestsufficient amount of energy to feed this sensor. Sample et al. [60] demonstrate that thepower transmitted from a 4 km distant TV station is capable of feeding a temperaturesensor. Moreover, a 30 cm by 20 cm antenna is sufficient to harvest around 60 µW yieldinga power density of about 0.1 µW/cm2.

2.3. Healthcare Informatics

With the digital transformation of patients data from manual spreadsheets to completeweb-based healthcare solutions, healthcare informatics have rapidly been evolved over thelast few decades. Web-based healthcare solutions combined together with IoT driven smartsystems enable universal accessibility of data for doctors, nurses, paramedics, healthcarestaff, and laboratories across the globe [61]. In this application domain, there have beennumerous implementations of RF energy harvesting sensor nodes for Wireless Body AreaNetworks (WBAN) [62–65] and medical wearables [66–68], including systems for patient

Sensors 2022, 22, 2990 8 of 36

health monitoring and wireless smart injection mechanisms [69]. Thanks to the integrationof RF energy harvesting capability, low-power medical devices can deliver real-time datasets and on-demand services are feasible due to the associated battery-free design [26]. Forexample, Ref. [62] et al. developed an implementation of a WBAN sensor comprising of asmall triple band antenna, a DC energy management and storage module, a sensing andcommunication module, and a micro controller to enable autonomous behavior. To convertRF energy harvesting into DC power, a triple band rectifier was designed for low powerapplications which successfully achieves 59% conversion efficiency for an input powerof −10 dBm. The proposed sensor is compact and suitable for self monitoring of humanbody in healthcare informatics. Similarly, Ref. [66] provides a comprehensive review onboth the scientific literature and commercially available RF power harvesting devices inwearable healthcare.

2.4. Radio Frequency Identification (RFID)

Radio Frequency Identification (RFID) [70] is one of the mature short-range wirelesstechnologies in which the data is digitally encoded in small radio transponders (alsoknown as tags or smart labels). RFID belongs to the technological family of AutomaticIdentification and Data capture, where tags need not be in line-of-sight for reading the datawithin the supported range. As RFID is based on a near-field technology, it is an efficientRF power and data transmission system. Numerous works have been identified using RFpower harvesting in a variety of RFID applications [71–78]. Mhatre et al. [71] describea three-phase energy harvesting system where they first describe a dual band antennacapable of supporting a large frequency response ranging from 900 MHz to 2.45 GHz andfair radiation patterns. In addition, the authors describe a rectenna for converting thereceived RF energy to DC voltage. The integration system provides a conversion efficiencyof 30% at 2.4 GHz.

Moreover, Ref. [72] overviews the RFID operation in more general terms and describesin detail a range of different RFID tags. The need and dynamics of RF power harvestingfor active RFID tags are identified and highlighted. In Ref. [73], RF energy harvesting isidentified as a source of green energy, suitable for a range of sensor applications in harshenvironments. Another study [74] provides a comprehensive review on the advancementson the implementations of RFID sensors with a particular emphasis on RF power harvesting,describing recent progress in the state of the art, and commercially available solutionswith innovative applications. Olgun et al. [75] discuss RFID rectenna design with theaim to harvest energy for powering up RFID from ambient energy sources operating inthe 2.45 GHz ISM (Industrial, Scientific, and Medical) band. Finally, Bukhtiar et al. [77]describe an RF energy harvesting system based on automatic input tuning for far-fieldRFID tags. A high quality factor LC-matching network is employed to boost the voltageof received RF power and then a proof of concept system is simulated in 0.13 µm CMOSdemonstrating a conversion efficiency of 50%.

2.5. Smart Buildings and Structural Health Monitoring

A smart building generally refers to a structure employing automated processes tocontrol the building operations (such as heating, ventilation, lighting, air conditioning, andsecurity, to name a few) in order to better understand the linkage between a building andits occupants to improve the living experience of inhabitants and energy efficiency of thebuilding [79]. Structural health monitoring aims to foresee the potential risks associatedwith the building structure before they actually cause any damage. Several scientificstudies [80–84] discuss the usefulness of RF energy harvesting to provide energy to sensorsdeployed in the building structures because of their high battery maintenance costs, asthe sensors are often installed in inaccessible places within a building or structure beingmonitored (such as bridges and tunnels) [82]. Ref. [80] describes the design of a rectennafor RF energy harvesting employing a compact wide band antenna operated at targeted8–18 GHz frequency bands yielding a DC output of 1.8V that can convert to 5–8 mV at

Sensors 2022, 22, 2990 9 of 36

20 dBm to power sensors in structural health monitoring applications. However, thegains achieved through such wide band antenna is relatively lower because of the powerdistributed across the wide frequency band.

Similarly, Ref. [81] demonstrate an implementation of sensors as a part of CPS to realizestructural health monitoring for smart buildings. The networks comprises of battery-freeLoRaWAN prototypes. One of them is powered by RF energy harvesting system operatedin the unlicensed ISM 868 MHz band. Extending the work in [81,82] exploits battery-freeLoRa motes directly fed by RF power harvesting to sense and report temperature andrelative humidity levels in structural health monitoring of smart buildings over a distanceranging from several meters to kilometers. In Ref. [83], the authors address the designand characterize a 66 cm2 sized rectenna operating at 868 MHz to power structural healthmonitoring sensing and communications applications. Lastly, Ref. [84] surveys recentadvancements in the ambient energy sources and power harvesting methodologies used tofeed sensors in structural health monitoring applications with a special emphasis on RFpower harvesting applications.

3. Dedicated vs. Ambient RF Energy Harvesting

The choice of wireless power harvesting technique to use in a particular applicationshould be considered carefully as it directly impacts the performance of RF power har-vesting systems and depends greatly on the requirements of the applications. Harvestingenergy from RF power sources differ from other harvesting techniques (such as solar, wind,thermal, and vibrational) primarily because of the following characteristics. RF sourcesare capable of providing a constant and controllable level of power over several distancesparticularly in fixed-size RF energy harvesting networks where the distance between trans-mitter and receiver is static and a known parameter. As the exact amount of harvestedpower depends on the distance of the receiver from the source, network nodes installed indifferent locations may observe significant difference in the amount of harvested energycompared with other nodes in the same network [31].

RF power harvesting can be classified into two different types: ambient and dedicatedRF power harvesting. RF energy harvesting associated with the natural sources presentwithin the environment can be referred to as ambient energy harvesting, and can generateenergy from sources such as digital TV, WiFi, and GSM signals. Dedicated RF sources canalso be used to harvest a required amount of energy for an application where ambient RFsources do not provide sufficient energy to meet the application requirements. This sectiondescribes the methodologies and prototypes from literature employing both ambient anddedicated RF energy harvesting techniques with the objective being to highlight the prosand cons associated with these techniques in different environments.

3.1. Dedicated RF Energy Harvesting Sources

Dedicated RF energy harvesting techniques involve installing dedicated sources ofenergy to enable power harvesting within the environment. These dedicated sources canemploy license-free ISM bands for power transfer. The commercially available powercastertransmitter [15] operating at sub-GHz band with 1–3 W transmission power is a goodexample of a dedicated RF source. However, this kind of dedicated RF harvesting systemcan incur high costs, especially in the case of large scale networks where large quantitiesof such sources may need to be installed. This might make the technology inappropriatefor some application deployments. Moreover, the maximum output power transmittedby these RF sources is governed by local frequency regulation agencies such as FederalCommunications Commission (FCC) [85] or general ISM regulations to avoid interferencewith other critical applications and ensure public safety.

There are a number of prototypes and implementations of dedicated RF harvestingsources available in the literature. For example, Zorbas et al. [86] modeled a network withsensor nodes capable of harvesting energy from RF sources and associated dedicated energytransmitters to power them up to enable sensing and communication of data. The authors

Sensors 2022, 22, 2990 10 of 36

identify the factors affecting the energy consumption of these nodes (such as networkdensity and the number of transmitted packets). Moreover, they also developed a methodfor multi-hop energy transfer between the nodes. An interesting contribution coming fromthis work is the cost analysis presented therein to establish if the cost incurred to designthese RF energy harvesting nodes can be compensated with a corresponding reductionin the level of maintenance costs for the system due to the presence of energy harvestingcapabilities.

Similarly, Radhika et al. [87] developed an RF powered sensor platform where theenergy could be harvested from either dedicated power sources or ambient RF poweredsources. The authors describe a set of requirements with a dedicated chip designed forthe sole purpose of power harvesting and conditioning. The designed hybrid system ispowered employing sub-GHz band frequencies for dedicated radiations demonstratinga system efficiency of 9.1% with 90 s transmission interval for dedicated power sourceand a maximum efficiency of 44% with 9.75 k resisting load in ambient harvesting source.Another interesting work [88] considers wireless powered networks with dedicated powerbeacons and multi-user nodes scenario where multiple user nodes are harvesting energyfrom power beacons and compete for the transmission. The user nodes have no otherpower source hence they are solely based on the energy harvested from the power beaconsand are assumed to be in harvesting mode, consumption mode, or idle mode. The authorspropose a charge scheduling scheme taking into account the behavior of power beaconsand user nodes to maximize the total harvested power.

Following the similar lines, Lakshmi et al. [89] come up with an efficient RF powerharvesting scheme where multiple dedicated RF harvesting sources were deployed to fulfillthe energy demands of a network. The work first focuses on the optimal location andthe number of these dedicated transmitters. The authors then devise a utility functiondescribing the placement of energy transmitters prioritizing the energy requirement ofrelay nodes and maintaining the minimal level of energy to provide for all the sensor nodeswithin a network.

Furthermore, Li et al. [90] propose wirelessly powered WSNs where the sensor nodesare harvesting sufficient power from a dedicated RF power source to share the informationwith the nearby communications data sink. The authors propose two different operationmodes: Frequency Division Multiplexing (FDM) to harvest energy and send informationsimultaneously over an orthogonal frequency band, and Time Division Multiplexing (TDM)to enable both energy harvesting and information transmission to be incorporated in thesame frequency band but in different time slots. Finally, various RF power transmis-sion schemes were introduced [91–93] considering dedicated mobile transmitters with anintention to feed different WSN applications.

3.2. Ambient RF Energy Harvesting Sources

Ambient RF harvesting refers to the technique of harvesting power from ambientsources which are freely available electromagnetic radiations in the environment in whichthe sensor node is deployed. There are typically many different radio signals in the airwhich are targeting different primary objectives (such as WiFi for the Internet connectivity,Terrestrial Television and Satellite for the broadcasting of TV channels, and Global Systemfor Mobiles (GSM) to enable mobile phone connectivity for voice, messaging, and data)that can be employed to harvest a small amount of energy. It is important to note thatthe level of magnitude and the predictability of harvested energy in the case of ambientenergy harvesting is comparatively lower than that which is available from dedicatedRF harvesting sources, but is still sufficient to provision sensor nodes in a variety ofapplications [94]. The transmitting power of all these ambient sources varies significantly,from 106 W transmitted from TV towers to 0.1 W from WiFi access points and mobilecommunications. Table 5 presents the average and maximum power densities of ambientRF power sources available at different frequencies taking into account the summation of

Sensors 2022, 22, 2990 11 of 36

all the spectral peaks in an entire band. The research community has extensively workeddealing with different aspects related to ambient RF harvesting sources.

For example, Pinuela et al. [95] evaluated the harvesting potential at different ambientRF power levels and frequencies on the streets outside all metro stations across LondonCity. Consequently, four harvesters were designed covering four different frequencies fromthe largest RF sources available in the metropolitan area such as DTV, GSM900, GSM1800,and 3G within the ultra high frequency spanning 0.3GHz to 3GHz. Prototypes weredesigned for each individual frequency band. The results demonstrate that almost half ofthe underground stations were deemed suitable for RF power harvesting when employingtheir designed prototypes. The output dc power density comparison was performed acrossall harvesters to demonstrate that RF harvesting can be competitive with respect to otherharvesting methodologies.

Following similar lines, Ref. [96] studied the feasibility of various ambient harvestingenergy sources through a preliminary assessment of power density and received power in asuburban area. The results demonstrated that received power available (i.e., −25 dBm from800 MHz) from a mobile base station is 13 dB higher than that available from digital TVbroadcasting. The authors designed and tested a circuit for RF–DC conversion and claimedthat a capacitor of 0.1 F could be charged up to 320 mV in 65 h from this ambient RF sourcepresent in a suburb. Another work [97] presents the measurement of power density ofambient RF harvesting sources and argue that density in broadband (1–3.5 GHz) is in theorder of 63 µW/m2. Two different systems have been considered to harness the ambientRF energy; a broadband system without a matching circuit and a narrow-band system witha matching circuit. The results conclude that the harvested energy is not sufficient enoughto directly feed a device but could be stored in a super capacitor or a micro battery to beutilized further.

Moreover, Ref. [98] reviews various ambient energy harvesting mechanisms with aspecial emphasis on RF power harvesting where they investigate a far-field low powerdensity technology enabled prototype of embedded micro controllers powered by ultrahigh frequency digital signals (512–566 MHz) where a broadcasting tower is 6.3 km awayfrom the harvester. A dual-band ambient RF energy harvester is employed at 915 MHz and2.4 GHz and an on-body harvester is also tested at 450 MHz to verify the potential of suchharvesters for smart skin applications. Furthermore, Ref. [99] estimates the ambient RFand microwave power that can be harvested through the omni-directional electromagneticsources studying the underlying microwave rectification at low power (i.e., −30 dBmor even lower) levels. A theoretical model is derived to accurately predict the rectifier’sefficiency and the insertion losses of matching networks. The results provide a comparisonof different microwave rectifiers with the proposed model which clearly exhibits a 10%improvement in the efficiency of the proposed model as compared to existing solutions.

Finally, Ungan et al. [100] have developed the concept of rectifying the energy fromlow power ambient sources to feed microsystems. The authors designed a circuit andgauged the efficiency of the RF harvesting system. An ambient radiation source of 1 µWand an impedance of 50 Ohm on 300 MHz was employed to match the impedance andresonant circuit transformation with a high quality factor of 25.

Ambient harvesting can further be classified into static and dynamic ambient har-vesting. The following subsections discuss both types of RF ambient harvesting with anexample for each type of implementations.

Static ambient RF power harvesting refers to the energy harvesting from RF sourcestransmitting relatively stable power over any period of time (such as television and radiotowers). Although the energy harvested by static ambient RF sources is more predictablecompared to the energy harvested through dynamic sources, it can still undergo short orlong term fluctuations due to varying service schedules and signal fading. As the powerdensity of static ambient sources is small at different frequency bands, a high gain antennais required for all these frequency bands and the rectifier should also be designed keepingin view the wide band spectrum. For example, Ref. [101] presents the performance analysis

Sensors 2022, 22, 2990 12 of 36

of a sensor employing stochastic geometry when powering it with static ambient RF energyharvesting sources and demonstrates that higher RF harvesting rates could be attained bythe sensors when the distribution of RF sources shows stronger repulsion.

Dynamic ambient RF sources are those working periodically and which transmitvarying powers at different times. This includes radio signals such as WiFi and primaryusers in cognitive radio networks. Hence, power harvesting systems generating energyfrom dynamic sources should be adaptive in nature, employing some kind of intelligence toestablish as to where the optimum frequency for power generation is in different frequencybands. Here, Ref. [102] provides a good example of RF harvesting from dynamic ambientsources in a cognitive radio network where secondary users are able to harvest RF energyfrom the primary users nearby. The wireless nodes can then utilize this energy to transmitdata when the primary users nearby are in idle mode or are sufficiently far away fromthe primary users. To summarize, there are certain pros and cons associated with bothdedicated and ambient RF sources which decides their applicability in a range of use cases.However, small power densities associated with ambient RF sources can limit its suitabilityfor some applications in different domains. Similarly, achieving longer distances betweenRF transmitters and harvesters is still a challenge as the range of energy transfer directlyinfluences the performance of the harvesting mechanism. Moreover, dedicated sources arebetter in terms of achieving higher conversion efficiency but the higher CAPEX (i.e., CapitalExpenditures) cost associated with the installation of dedicated RF power chargers may bea concern. Furthermore, site selection associated with the installation of dedicated chargerscan play a significant role in optimizing the range between transmitters and receivers ina network architecture definition. Finally, antenna and rectifier design for static ambientRF harvesting systems should be considered in order to maximize a stable amount of RFenergy being harvested.

Table 5. Power densities of ambient RF power harvesting sources in different frequency bands.

Band Frequency Range Average Power Density Maximum Power Density(MHz) (nW/cm2) (nW/cm2)

DTV [95] 470–610 0.89 460GSM900 (MTx) [95] 880–915 0.45 39GSM900 (BTx) [95] 925–960 36 1930GSM1800 (MTx) [95] 1710–1785 0.5 20GSM1800 (BTx) [95] 1805–1880 84 63903G (MTx) [103] 1920–1980 0.46 663G (BTx) [103] 2110–2170 12 240Wi-Fi [103] 2400–2500 0.18 6

4. RF Energy Harvesting Evaluation Metrics

In this section, the key performance metrics associated with the evaluation of RFpower are described in the context of energy harvesting. There are a number of parametersthat should be evaluated which can play a decisive role in the performance of an RF energyharvesting design and implementation. These evaluation metrics are defined by the un-derlying application requirements and can change according to the application scenario.The most relevant metrics used for evaluating RF power harvesting systems include stan-dard parameters such as the range between transmitter and receiver, the efficiency of theharvesting system, the harvester sensitivity, the resonance factor of a resonator, and theoutput power obtained at the receiver [104]. However, evaluating the trade-offs betweenthe metrics (e.g., the operation range and efficiency) is vital in the comparison of thesemetrics. Moreover, some other related factors such as bulk manufacturing availability,maturity of fabrication process, and low cost design can also be factors to consider in theselection of an RF energy harvesting technology.

Following are some of the key evaluation metrics in the domain of RF energy har-vesting which can significantly impact on the efficiency of the harvesting system. The

Sensors 2022, 22, 2990 13 of 36

main objectives are to achieve the optimal power conversion efficiency, output power, andreceiver sensitivity of the RF harvesting systems and the choice of optimal operationalfrequency of the incident signal is critical in RF energy harvesting systems.

4.1. Range and Frequency

The operating distance or range between the energy transmitter and receiver is oneof the key RF power performance metrics to be considered (see Figure 2). This is relatedto the operational frequency of the system in question. For example, systems whichare transmitting at higher frequencies suffer more from attenuation as compared to lowfrequency transmissions due to the longer wavelength when passing through the wirelessmedium. However, low frequency signals penetrate deeper through matter comparedto their counterparts. For instance, the transmitting frequencies should not exceed a fewmegahertz in case the target application of RF power harvesting is an implantable device.

Figure 2. The impact of distance on the received power in RF power harvesting.

4.2. Conversion Efficiency

Power conversion efficiency (PCE) is another critical metric when comparing perfor-mance of RF harvesting systems. This is the ratio of the power applied to the load versusthe power gained by the antenna. Here, the RF-DC power conversion efficiency not onlyrefers to the efficiency of a rectifier, but the voltage multiplier and the storage elementas well. RF transmission losses are not usually considered in this conversion efficiencycalculation. This can simply be expressed as the ratio of the power delivered to the sourceto that of retrieved power as follows [104]:

ηPCE =Pload

Pretrieved. (1)

where Pload is the power applied on the load and Pretrieved is the harvested power at theantenna. A comparison of power conversion efficiency against different levels of receivedRF power can be seen in Figure 3 when different levels of loads are applied (such as10 kΩ, 60 kΩ, 110 kΩ, and 160 kΩ) using a Schottky diode with a conventional CMOSrectifier topology.

Sensors 2022, 22, 2990 14 of 36

Figure 3. Power conversion efficiency vs. received RF power against different loads.

4.3. Resonance Factor

Resonance occurs when a system is able to store and easily transfer energy betweendifferent storage modes, such as Kinetic energy or Potential energy (as in case of a simplependulum). Most energy harvesters are designed to work at resonance frequency in orderto obtain maximum output power. Resonant frequency is the oscillation of a system atits natural or unforced resonance. The larger the antenna size, the lower the resonancefrequency. Hence, resonance factor (also known as Q factor) is defined as the genericdimensionless value describing the strength and bandwidth of resonance. The Q factorrepresents the increase in the peak voltage when the system needs to resonate on resonantfrequency. This derives the expression of Q factor as follows [105]:

Q = 2πEstored

Edissipated(2)

where Estored is the total stored energy and Edissipated is the dissipated amount of energyper cycle. Hence, it can be inferred that a high Q factor refers to the narrow resonantbandwidth but high voltage gain. Moreover, Equation (2) also implies that Q factor isinversely proportional to the amount of dissipated energy during each discharge cycle.

4.4. Sensitivity

Sensitivity is defined as the minimum amount of incident power required for triggeringa system’s operation. It is the ability to harvest energy and operate at a low power density.The greater the sensitivity of a harvesting system, the better the conversion efficiency dueto the strength of incident signal and hence the performance of the system. Sensitivity canbe quantified by the following expression [104]:

S(dBm) = 10 log10Pmin

1 mW(3)

where Pmin refers to the minimum power a system is required to accomplish a task. It ispertinent to note that threshold voltage of CMOS influences sensitivity as lower thresh-old CMOS offers more sensitivity but on the cost of higher current leakages, ultimatelycompromising on the system efficiency.

4.5. Output Power

Output power is another key metric for the performance evaluation of power har-vesting systems which is usually described in the form of DC power, a product of voltageapplied by the load (V) and the current (I). The measurement of the load voltage demon-strates the performance of a system which depends on the load impedance. For example,

Sensors 2022, 22, 2990 15 of 36

in the case where the load is a sensor, the voltage (V) is more important than the current(I). Contrarily, in the applications incorporating LEDs or electrolysis, I is more dominantcompared to V.

5. Energy Propagation Models

It is essential to evaluate the propagation characteristics of a radio system in order toprecisely estimate the set of signal parameters that are needed to measure the quality andperformance of an RF signal associated with a harvesting system. The analysis of signalpropagation enables us to make an appropriate estimation of the signal characteristics. Theaccurate prediction of the behavior of a radio signal is critical to the system design for futureRF harvesting systems. Moreover, propagation models are a low-cost alternative to physicalsite examination and measurement which makes optimum deployment more feasible byanalyzing the propagation models for the deployment site [106]. This section reviewspropagation models for different RF energy harvesting systems available in the literature.

5.1. Deterministic Models

It is pertinent to note that the amount of harvestable RF energy depends on thetransmitting power of the source, the distance between the source and the harvester,and the wavelength of the RF signal transmitted and received by a receiving antenna.Deterministic models characterize the electromagnetic radiation propagation based on aset of deterministic parameters which are known in advance. The following are the twodeterministic propagation models most widely considered for studying the propagationbehavior of RF signals.

5.1.1. Free-Space Model

The free space propagation model is a model which can help in understanding thebehavior of RF signals. The transmitted power of an RF signal in free space can be evaluatedbased on the famous Friis Equation [107]:

PR = PTGTGRλ2

(4πd)2L(4)

where PR, PT , L, GT , GR, λ, and d are the received power at the harvester, the transmitpower from the source, the path loss factor, the gain of the transmitting antenna, the gainof the receiving antenna, the wavelength of the RF signal, and the distance between thetransmit antenna and the receiver antenna at harvester, respectively. The free space modelis simplistic model assuming that there is only a single path between the transmitting andreceiving antennas while this is not always the case.

5.1.2. Two-Ray Ground Model

The two-ray ground model [108] is another propagation model that comes into play inthe presence of scattering and reflection of the antecedent RF signal. Due to this scattering,the received RF signal reaches the harvester after passing through multiple paths andas such, the free space model is unable to provide accurate estimations. The two-raypropagation model takes into account both the line-of-sight signal and reflected signal toresolve this problem. Hence, the harvested RF power from transmitter presented by thetwo-ray ground model could be expressed as:

PR = PTGTGRh2

t h2r

d4L. (5)

while ht and hr are the heights of the transmitting and receiving antennas, respectively.

Sensors 2022, 22, 2990 16 of 36

5.2. Probabilistic Models

Unlike deterministic models, probabilistic models deduce parameters based on dis-tributions which allow more realistic modeling compared to deterministic parametersmodeling. The Rayleigh model [109] is one of the more widely used and applicable prob-abilistic propagation models capable of representing the scenarios where a line-of-sightchannel between the transmitter and receiver is absent. Hence, the transmit power of RFsignals expressed by Rayleigh model could be as follows:

PR = PdetR × 10L × |r|2, (6)

where PdetR indicates the received RF power estimated by the deterministic models in

Equations (4) and (5). The path-loss factor L is defined as follows [109]:

L(d) = L(d0) + 10alog(dd0

) + G, (7)

where d is the distance between the transmitter and the receiver, L(d0) is the mean path-loss fora reference distance d0, a is the path-loss exponent, and G is a zero-mean Gaussian distributedrandom variable with variance σ [109]. The cumulative amount of RF harvested power couldbe evaluated based on both the type of the network model and chosen RF propagation model.For instance, Ref. [110] considers the transmitters to be distributed spatially as per the Poissonprocess and characterize the distribution of RF power received at the harvester. They providethe theoretical estimation for the distribution of RF power received at the harvester taking intoaccount the fading effects and path loss. The authors propose a probabilistic model based onthat RF power distribution to find the probability of a node to transmit a packet after having acertain recharge time. The results taken from their analysis were encouraging and quite closeto the simulation results that justified the accuracy.

Moreover, Ref. [111] models the electromagnetic energy harvested by RF radiationsthrough the gamma process for energy harvesting systems. The characterization of theharvesting energy is achieved from the Nakagami-m model as a continuous-time stochasticprocess defining the signal reception in a number of frequency channels. The transmissionpolicy is defined considering different fading conditions and a set of performance measuresis presented to analyze the performance of harvesting system. Furthermore, Ref. [112]characterizes the distribution of RF power received by the harvester acknowledging thattransmitters are spatially distributed through Poisson process. The authors demonstratethat the RF received power can be approximated by the summation of multiple gamma dis-tributions with different parameters for scale and shape taking into account the shadowingand Rayleigh fading. The authors eventually derive the probability of a node to transmit apacket after a specific charging time. The numerical results demonstrated herein are quiteclose to the simulated ones which argues about the validity of the proposed system.

6. Rectenna Architecture for RF Energy Harvesters

The rectifying antenna (i.e., rectenna) circuit architecture is of central importance inthe context of performance optimization of RF energy harvesting (see Figure 4). It serves toreceive RF signals in a single frequency or a range of different frequencies and convertsthem into DC power. The quality of a rectenna can be gauged by measuring the conversionefficiency it offers while operating in different frequency bands [113]. This section coversdifferent aspects of the rectenna design starting from considerations of an antenna designand then reviewing the state of the art available on both single and multi-antenna RFharvesters, impedance matching networks, and rectifiers. Moreover, the reader can refer toa related recent review on antenna technologies for ambient RF energy harvesting [114].

Sensors 2022, 22, 2990 17 of 36

Figure 4. Rectenna architecture for RF power harvesting system.

6.1. Antenna Design Considerations

The antenna is one of the integral components of RF power harvester which is pri-marily responsible for collecting the RF power from a transmitter to transfer it to the restof the rectenna circuit (such as matching network, rectifier, and low pass filters) for con-version into DC power and feeding the load. Hence, the antenna design is of paramountimportance in obtaining the maximum power transfer efficiency. There are certain chal-lenges in the antenna design that can broadly be classified into conversion efficiency, formfactor, and bandwidth. Some of the key design considerations for antennas are discussedthroughout this subsection that could help to maximize efficiencies and achieve the desiredperformance objectives.

6.1.1. Antenna Characteristics

Thanks to recent advancements in very large scale integration technologies, the elec-tronic circuits and systems are consistently being miniaturized following the demands ofconsumer of compact size through the use of embeddable components including antennas.However, achieving this milestone is not straight forward and involves a trade-off as re-gards a range of parameters such as bandwidth, beamwidth, efficiency, and output gain.The limits on the antenna size and efficiency are first discussed by [107].

Hansen and Best [115] have shown that the lower bound of the radiation quality factorQ—which measures how many times the current passes through the antenna circuit—depends on the efficiency η:

Q ≥ η( 1

k3a3 +1ka

), (8)

where a is the radius of the spherical radiation and k = 2πλ .

It is pertinent to note the trade-off between antenna size and radiation frequency andantenna designers need to have an optimized design methodology so as to maximize theefficiency while minimizing the size of the antenna at the same time. The Q-factor of anantenna can be described as follows :

Q =CFBW

, (9)

where CF is the central frequency and BW the bandwidth. Hence, in order to achieve ahigher value of Q-factor, the bandwidth has to be narrow and vice versa. Similarly, reducingthe size of the antenna decreases Q and so the bandwidth tends to increase. It is significantto note that since the product of bandwidth and antenna gain remains constant, increasingthe bandwidth would cause a decrease in the gain. Hence, a careful consideration isneeded to analyze the trade-off among these antenna design parameters. The fundamentalchallenge in this case is to design the miniaturized antennas for the least performancedegradation. The simplest way to achieve this could be by using high dielectric constant

Sensors 2022, 22, 2990 18 of 36

materials exhibiting extremely low losses which enables trimming the antenna size due tothe reduction in the size of guided wavelength but also exhibits narrow bandwidth andlow gains caused by the excitation of surface waves [116]. However, there could be severalother approaches on how the miniaturization could be achieved (such as slits, shortingposts, slots, or geometric optimization, etc.) [117].

6.1.2. Polarization

Antenna polarization is one of the key aspects as regards its design that actually refersto the polarization of the radiated fields of an antenna which could be classified into linearlypolarized and circularly polarized antennas. The later antennas are more prominent in the contextof RF energy harvesting due to their capability to radiate and receive RF energy in any planewithout high losses [118]. It also helps in improving the power conversion efficiency byreducing the polarization mismatch losses. However, the method employed to accomplishcircularly polarized radiations appears to be a limitation as it results in narrow bandwidth [119].The methods used to accomplish the circular polarization include corner truncation, slits, spurlines, stub-loading, and cross shaped slots in the antenna design. Ref. [120] reports a designincorporating an annular ring around a circular patch for circular polarization that achievesa high conversion efficiency in the order of 25% at 0 dBm input but which starts decreasingdrastically at −20 dBm. As a limitation, it employs thin and flexible substrate that tends tosuffer from bending losses which ultimately affects efficiency [120].

6.1.3. Harmonic Rejection

Several non-linear components in rectenna design cause the harmonics of fundamentalfrequencies. These harmonics re-radiate to cause electromagnetic interference with theantenna transmission and the associated rectenna circuit leading to a the degradation ofthe performance of the overall circuit. Low pass filters need to be added between theantenna and rectification circuit to overcome this issue but at the cost of increase in sizeand complexity [121]. Harmonic rejection is more important for high-power RF energyharvesting cases as harmonics are more severe in the high-power range. A range of an-tenna design approaches [122–125] have been proposed (also known as Filtennas) with theharmonic rejection aiming to maximize the received power on a specific frequency andtrying to suppress harmonics with the help of structural modifications (such as stub, slit,and Defected Ground Structures). For example, Razavi et al. [126] present a comprehen-sive study on the Harmonic Rejection Mixers (HRM) that overviews the HRM operationprinciples and design issues with the intention to minimize the amount of high frequencyfilters in the rectenna design and the continuously increasing demand for wide-band radios.Moreover, Forbes et al. [127] design an HRM with programmable frequency which enablesmultiple effective frequencies in a single HRM without modifying the analogue signalpath. A dissertation [128] also throws light on the novel architectures for HRM to achieve arefined level of harmonic rejection by reducing the mismatching sensitivity.

6.1.4. Reconfigurability

Polarization and frequency diversification in wireless systems instigated the devel-opment of re-configurable antennas in RF energy harvesting systems [118]. This can beaccomplished by modifying the antenna structure which causes a change in its physicalproperties that helps bandwidth enhancement and size reduction as only a single antennais able to operate at different frequencies [129]. To this end, Shao et al. [130] attain a wideimpedance match employing a two-by-two phase array where beam steering was achievedmodifying the feed position in a mechanical way. They control the phase difference betweenthe radiating elements by adjusting the wave path within the transmission line. A recentlypublished survey on re-configurable antennas [131] nicely outlines the switching and imple-mentation techniques with multi-mode and cognitive radio operations. The authors arguethat electronic switches are among the popular options to design re-configurable antennasbecause of their ease of integration, efficiency, and reliability. Another comprehensive

Sensors 2022, 22, 2990 19 of 36

survey [132] identifies re-configurable antennas most suited for today’s wireless systemsbecause of their characteristics such as multi-functional capabilities, good isolation, mini-mized volume requirements, sufficient out-of-band rejection, and low front end processingefforts without a filtering element, to name a few. The authors survey the recently proposedre-configurable antenna designs that are suitable for a broad variety of use cases of wirelesscommunications including, but not limited to, 4G/5G, Ultra Wide Band (UWB) communica-tion, and Multiple-Input Multiple-Output (MIMO) systems. Finally, various proposals havebeen summarized in Table 6 taking into account a range of antenna design parameters suchas polarization technique, harmonic rejection, operating frequency, bandwidth, antennagains in terms of percentage efficiency, and re-configurability that have been discussedthroughout Section 6.1.

Table 6. A chronologically ordered summary of indicative harmonic rejection and re-configurableantennas for RF power harvesters.

Antenna Feature PL * HR ** Frequency Bandwidth Gain (dBi)/ RC †Proposal (GHz) (MHz) Effic. (%)

Polarization Circular patch

No 5.07–5.86 0.79 3 Yesreconfigurable with reconfigu- 1 LP,monopole antenna rable feed 2 CP[133] antenna

T-shaped slot T-shaped condu-wideband antenna ctor line conne- CP Yes 2–3.5 1.5 5.5 No[134] cted to path

Metamaterial based Antenna loaded OFF: 7–8.5 OFF: 1.5 OFF: 1.93multiband antenna with interdigital LP No ON: 3.8–4.2, ON: 0.4, ON: 1.78, Yes[135] capacitor slots 5.5–6, 6.8–8.5 0.5, 1.7 1.63, 2.32

Switchable 3-loop resonatorsLP Yes

OFF: 3.2–117

OFF: 4.33,YesFiltenna [136] in UWB antenna ON: 3–3.5, ON: 3.8

4–5.7, 6.2–11 96%

Off center fed Dipoles areCP No 1.8, 2.5 0.7 3.5 Nodipole antenna modified into

[137] bow tie stubs* Polarization: CP = Circularly Polarized; LP = Linearly Polarized, ** Harmonic Rejection, † Reconfigurable.

6.2. Single and Multi-Antenna RF Harvesters6.2.1. Single Antenna RF Harvesters

As the antenna design involves a range of constraints (such as space and costs another considerations described above), the research community has striven to optimize theoperation of a single antenna design operated at one or several frequency bands as a part ofRF power harvester. Omni-directional antennas are one of the obvious choices for RF energyharvesting due to their simple design and 360° coverage to receive and handle the radiatingsignal in conjunction with a reasonable conversion efficiency. Some of the antennas inthis category achieve high efficiency on single frequency (e.g., 2.45 GHz) [138,139], whiledual-band [140–145], broadband [146–151], and multi-band antennas [152–154] are alsoreported in the literature and will be outlined throughout this subsection.

Dual-Band Antennas for RF Energy Harvesting

Sun et al. [140] present a dual band antenna for harvesting ambient energy whileoperating on Global System for Mobile communication (GSM)-1800 and Universal MobileTelecommunication Systems (UMTS)-2100 frequency bands, that is based on a broadbandquasi-Yagi antenna array with higher gains and bandwidths. Moreover, the dual-bandrectifier they propose effectively improves the conversion efficiency for ambient RF power.The results demonstrate a conversion efficiency of 40% and an output of 224 mV on a5 kΩ resistor with an input power density of 455 µW/m2. Similarly, Bakkali et al. [141]propose a dual-band antenna design for ambient RF energy harvesting from commercial

Sensors 2022, 22, 2990 20 of 36

broadcasting stations. The authors present several antenna designs aiming to improve thepower conversion efficiency of the overall rectenna system as compared to 2.45 GHz and5 GHz (i.e., WiFi bands). The designed dual band antenna is suitable to be integrated withRF energy harvesting systems and is capable of feeding a range of sensor nodes installed inharsh industrial environments.

The same authors developed a design for a receiving antenna [142] to more effectivelyharvest the RF energy due to the performance optimization to increase the antenna gains inWiFi bands such as 2.45 and 5 GHz. The return loss in this case goes below −29 dBm for5 GHz frequency band and the radiation shape exhibits omni-directional patterns. Anotherwork [143] presents a Koch-like dual-band antenna design for RF power harvesting whichtargets GSM-900 and WiFi frequency bands to harness energy. The simulations and theextensive measurements carried out herein to assess the functionality and performance ofthe proposed approach justify the superiority of the dual-band Koch-like antenna designover conventional counterparts.

Similarly, Singh et al. [144] propose an open ended microstrip based compact dual-band antenna targeting UMTS and RFID signals. This antenna design has a Sierpinskifractal geometry aiming to cover 2.1 GHz and 5.8 GHz bands. A harmonic suppressionmethod is employed in order to improve the RF-DC power conversion efficiency in boththe supportive bands. The results demonstrate that a power conversion efficiency of 79%and 86% are achieved for a 1 kΩ on 2.1 GHz and 5.8 GHz, respectively, which compareswell to the state of the art in the area. Finally, Jei et al. [145] propose a circularly polarizeddual-band antenna design along with a compact rectifier for RF power harvesting systems.The prototype of a compact rectifier is presented and measured with dual band targeting0.9 and 2.45 GHz where the conversion efficiency was achieved as 40% at 0.9 GHz and 39%on 2.45 GHz, respectively.

Broadband Antenna for RF Energy Harvesting

A range of proposals for broadband antennas have also been presented in the recentpast. For example, Arrawatia et al. [146] propose a broadband omni-directional antennafor RF power harvesting offering a bandwidth from 850 MHz to 1.94 GHz which is capableof receiving horizontal and vertically polarized radiating waves with stable pattern for thewhole bandwidth. A power conversion efficiency of 60% and 17% are achieved for a 500 Ωload operating at 980 MHz and 1800 MHz, respectively. Consequently, a voltage of 3.76 Vfor an open circuit and 1.38 V across 4.3 kΩ load is achieved at 25 m distance. Similarly,Song et al. [147] propose a broadband antenna for ambient RF energy harvesting coveringthe broad spectrum from 1.8 to 2.5 GHz. The authors also design an impedance matchingcircuit towards improving the performance and efficiency of the overall rectenna for lowinput power levels. The proposed dual band antenna has a built-in harmonic rejectionmechanism to further refine the antenna efficiency. It offers a measured sensitivity of−35 dBm which leads to the conversion efficiency of 55% on the input power of −10 dBm.The results demonstrate that the output power offered by this design is better than theother proposed antennas with similar size and ambient conditions.

Another work [148] presents the design of a broadband antenna for RF energy harvestingsupporting a broad spectrum of 900 MHz to 3 GHz. The authors study various issuesrelated to RF design and performed several experiments to improve the efficiency of therectenna circuit. The results presented herein exhibit that the power of −20 dBm couldbe harnessed through this kind of system. The connection orientation is also studied byexamining both the serial and parallel connection of two RF harvesters compared to a singlesystem. Moreover, Singh et al. [149] propose another compact broadband antenna for RFpower harvesting applications which employs a Graphene Field Effect Transistor technique toenhance the impedance bandwidth of the rectifying circuit aiming to cover a frequency rangeof 22.5–27.5 GHz. The proposed antenna is capable of attaining a conversion efficiency of 80%at 5 dBm for the 5 kΩ load yielding an output voltage of 6.8 V.

Sensors 2022, 22, 2990 21 of 36

Moreover, Shi et al. [150] present a compact broadband antenna for ambient RFharvesting with great bandwidth and matching characteristics. The authors also investigatea rectifier with a single stub matching network for improved impedance matching andRF to DC conversion efficiency even with low input power. A series of simulation andmeasurements are performed to gauge the performance of the proposed antenna andrectifier where the taken measurements support the simulation results. The results arguethat the highest conversion efficiency is recorded as 64% at 0 dBm where the achievedoutput voltage is 1.5 V. A design of a coplaner waveguide-fed broadband antenna ispresented in [151] for RF energy harvesting applications, that was designed to operate at5.8 GHz ISM band. The antenna prototype was designed using poly-tetrafluoroethylenedielectric material which could achieve a peak antenna gain of 5.85 dBi operating on a5.8 GHz band. The simulation results argue about achieving the maximum RF to DC powerconversion efficiency of 88% at the load of 1 kΩ.

Multi-Band Antennas for RF Energy Harvesting

Multi-band antennas have also been studied for many years due to their configurationflexibility, compact size, and leverage to operate on several frequency bands through asingle antenna design. For instance, Song et al. [152] developed the concept of a six banddual circularly polarized antenna with an improved impedance matching technique whichis useful to maintain the performance level of a rectenna in varying power conditions. Theproposed multi-band antenna has a wide bandwidth (i.e., 550 MHz to 2.5 GHz) and anannular ring structure and a feeding technique was taken into account to optimize thesize and performance of a multiband antenna. Similarly, the optimal load of 10–75 kΩ isused for a constant power conversion efficiency. The results show that 26 and 8 µW is themaximum harvesting DC power in outdoor and indoor environments, respectively.

Another work [153] proposes a multi-band dual polarized antenna for RF energyharvesting which employs a proximity coupled arrangement to improve the impedancebandwidth so the complete C-band (4–8 GHz) could be covered. The proposed designis compact and dual circularly polarized at three different bands at −10 dB impedancebandwidth. It is evident from the results that a conversion efficiency of 84% could beachieved at 5.76 GHz. Finally, Chandranwanshi et al. [154] present the design of a tripleband differential rectenna for RF harvesting which was designed to operate in UMTSfrequency spectrum (i.e., 2.1 GHz), WiFi (i.e., 2.4 GHz), and WiMAX (3.3–3.8 GHz). A multi-band slot antenna is employed to design the proposed rectenna with an antenna gain of 7,5.5, and 9.2 dBi is attainable at 2, 2.5, and 3.5 GHz, respectively. The measurement resultsdemonstrate that a maximum power conversion efficiency of 53%, 31%, and 15.5% couldbe achieved while operating at 2, 2.5, and 3.5 GHz, respectively.

6.2.2. Multi-Antenna RF Harvesters

The single antenna transmitters covered throughout the last subsection generallysuffer from a reduced transfer efficiency as they generate omni-directional radiations oftransmitted RF signals. This reduction in transmission efficiency demands for advancedmulti-antenna RF harvesting techniques (such as beamforming) to attain spatial multiplex-ing which yields improving the efficiency of RF power without additional bandwidth orincreased transmit power.

The first effort towards multi-antenna RF powered networks for evaluating thethroughput performance for energy beamforming was done by Haung et al. [155]. Theauthors consider a network consisting of a hybrid Access Point (AP) with multiple antennasand a single user where the user has no consistent power supply and the energy is har-vested by the broadcasted signals by the AP. They also contribute by deriving closed-formexpressions for the delay-limited and delay-tolerant transmissions which are validatedthrough simulations. Finally, the impact of a range of parameters (such as transmit power,harvesting time, throughput, and the number of antennas) is evaluated. Another work [156]proposes the idea of taking into account downlink non-orthogonal systems where the Base

Sensors 2022, 22, 2990 22 of 36

Station (BS) is equipped with multiple antennas and caters Relay User (RU) and Far User(FU). Here, the RU harnesses energy from the RF signals received by BS to accomplishrelay operation for FU. The authors consider three different types of relaying protocols atRU (such as amplify-forward, decode-forward, and quantize-map-forward) employingbeamforming and random antenna selection strategies at BS and RU. The closed formexpressions are further derived for the outage probability of relay protocols and antennastrategies.

Huang et al. [157] evaluated the performance of transmit antenna selection schemesfor a decode-and-forward RF energy harvesting relay cooperation network. The authorspresent an idea of power-splitting scheme where the relay harvests some amount of energyfrom the received signals and then employs the same energy to forward the signal to thedestination. The authors also derive the analytical expressions for outage probability (aspresented in [156]) and then tractable asymptotic outage probability of the network withdifferent transmit antenna selection schemes in an effort to characterize coding gain anddiversity order in higher Signal-to-Noise Ratio (SNR) regimes. The simulation resultsclearly demonstrate the following three aspects: the second sub-optimal scheme relativelyachieves a comparable performance with respect to the optimal scheme with the reducedimplementation cost, the relay location is critical to outage performance and optimalpower-splitting rate, and the feedback delay is vital towards evaluating the diversity order.

Moreover, Minhong et al. [158] propose the employment of multiple harvestingantennas in an effort to enhance the amount of energy harvested from an energy harvestingdevice in a given space. The authors used four different cooperating antennas installedin less than twice the square area required by a single antenna and the achieved resultsidentified the suitability of this method for improving the amount of harvested energy.Lastly, Mrnka et al. [159] surveyed different types of antennas for RF power harvesting. Theauthors identified the following four different types of antennas and marked them suitablefor RF energy harvesting: patch antenna, dielectric resonator antenna, modified invertedF antenna, and slot antenna. These antennas were compared on the basis of dimension,reflection coefficient, radiation patterns, and conversion efficiency.

6.3. Matching Networks

Impedance matching circuits are an essential component of RF energy harvestingsystems and are crucial to the performance optimization of the overall harvesting system.There are a number of techniques for signal modeling to estimate the behavior of devicesusing linear equations to design impedance matching circuits (e.g., low noise amplifiers).However, these techniques are not suitable to use with RF power harvesting systemsbecause of a very large input signal and the absence of DC bias which leads to the degrada-tion of power conversion efficiency caused by impedance mismatch between rectifier andantenna [160].

Hence, a robust impedance matching network design is desired for achieving a sig-nificantly greater power conversion efficiency that has received much attention from theresearch community. For instance, Hameed et al. [161] propose a systematic technique fordeveloping impedance matching circuits of RF power harvesters towards optimizing theoutput for a number of input power levels. The experiments demonstrate that the highestamount of harvested energy in case of a fixed input power is achieved when the transistorsare biased with stable DC operating voltage. Similarly, the authors also propose a selectionstrategy for matching networks in the case of variable power levels in order to maximizethe expected harvested energy based on the Probability Density Function (PDF) of a giveninput power level. The proposed RF power harvester demonstrates a maximum powerconversion efficiency of 32% at −15 dBm and capable to provide an output voltage of 3.2 Vto a load of 1 MΩ.

Similarly, Felini et al. [162] propose an improved dynamic impedance matching net-work scheme for RF energy harvesting systems. The RF input required for a standardizedoperation in this scheme is −10 dBm allowing a distance of 1.5 m from a source of 30 dBm.

Sensors 2022, 22, 2990 23 of 36

The proposed system is fabricated employing FR4 substrate using discrete components thatis able to power general purpose devices by converting RF energy to a DC voltage. Theresults clearly exhibit the capabilities of the system with optimized impedance matchingand received RF power up to +5 dBm. Finally, Agrawal et al. [163] present an analysisof different matching circuit techniques towards realizing efficient RF energy harvestingsystems. The authors present RF harvesting systems with three different approaches: res-onators, number of multiplier stages, and low pass filters. Resonators are important inimproving the amplitude of input signal and are capable of enhancing the performance byup to 30 times. The L type network serves as a resonator as well as matching circuit in theproposed system at resonant frequency and yields maximum power conversion efficiencyof 79% on a load of 50 kΩ at an input power of −10 dBm.

6.4. Rectifiers/Voltage Multipliers

Rectifiers (also known as voltage multipliers) are another significant subsystem in theRF energy harvesting systems as they are responsible for converting ambient RF harvestedpower into a useful DC voltage that can be fed to a load. Thus, the performance of aRF energy harvesting system usually depends on both the quality of receiving antennaand the efficiency of a rectifier. It is pertinent to note that the nonlinear component is thecore for converting RF signals into DC power. Based on their topological structure andsymmetry property, rectifiers could be classified as single-ended (i.e., voltage output) topol-ogy [164–166] and differential topology (i.e., full wave differential precision) [28,167,168]rectifiers to rectify the higher voltages. The rectifiers used in RF energy harvesting cir-cuits could also be classified based on the frequency bands they cover (such as dualband [169–171] and triple band [172–174] rectifiers).

There have been a number of proposals for rectenna circuit architectures for RF energyharvesting described throughout this section from single to multi-band rectenna and multi-antenna architectures with different power conversion efficiencies, as shown in Table 7.However, there are several open issues and prospective research directions when it comesto rectenna design. First, Antenna design with maximum power transfer efficiency isstill a challenging task with plenty of variables involved. Second, the choice of a bestpolarization technique for optimum power conversion efficiency is another open issue.Third, antenna orientation significantly influences the performance and, hence, the selectionof best orientation of the antennas is inevitable for optimum power conversion efficiency.

Table 7. Summary of power conversion efficiencies achieved on different frequency bands and loads.

Reference Antenna Frequency Band Power ConversionType * Efficiency, PCE (%)

Mhatre et al. [71] SB 2.4 GHz 30%Sun et al. [140] SB GSM-1800 MHz and UMTS-2100 MHz 40% @ 5 kΩSingh et al. [144] DB 2.8 GHz and 5.8 GHz 79% and 86% @ 1 kΩArrawatia et al. [146] BB 850 MHz to 1.94 GHz 60% and 17% @ 500 ΩSong et al. [147] BB 1.8 to 2.5 GHz 55% @ −10 dBmSingh et al. [149] BB 22.5 GHz to 27.5 GHz 80% @ 5 dBm & 5 kΩsaranya et al. [151] BB 5.8 GHz to 5.85 GHz 88% @ 1 kΩSingh et al. [153] MB C-band (4–8 GHz) 84%Chandra et al. [154] TB 2 GHz, 2.5 GHz, and 3.5 GHz 53%, 31%, 15.5%Hameed et al. [161] SB 902–928 MHz 31% @ 1 MΩAgrawal et al. [163] SB 900 MHz 79% @ 50 kΩ

* SB—Single Band; DB—Dual-Band; TB—Tri-band; BB—Broadband; MB—Multi-band.

7. Medium Access Control Protocols for RF Power Harvesting

A Medium Access Control (MAC) protocol defines the way the devices access themedium and, thus, they communicate with each other. A common access way is the carrier-sense multiple access (CSMA) where a device checks if there is an ongoing transmissionbefore starting its own transmission on the same channel. If the channel is occupied, thetransmission is postponed—usually for some random time—until the node tries again.

Sensors 2022, 22, 2990 24 of 36

Another common way of accessing the medium is the network coordinator to give to eachnode a usually unique time slot and/or frequency (channel) to perform transmissions. Thistechnique is called Time-division multiple access (TDMA) and Frequency-division multipleaccess (FDMA) for time slots and frequencies respectively. Combinations between CSMA,TDMA, and FDMA solution may exist.

The choice of an appropriate MAC protocol plays a critical role in the design andperformance of RF energy harvesting systems. As communication primitives are known tobe responsible for a large portion of energy expenditure in a wireless sensor node quotaspent to satisfy the demands of different communication primitives, the design of theMAC protocol is crucial to ensure appropriate sleep intervals for RF energy harvestingand energy optimal operations without impacting on quality of service constraints forcommunications [175]. Many research works in this space exist investigating the designand evaluation of MAC protocols with energy harvesting support ([176–178] to mentionsome notable ones). This section focuses on reviewing the MAC protocols designed forRF energy harvesting systems. Table 8 summarizes the main features of the protocolsdescribed in the next paragraphs.

Kim et al. [179] propose a MAC protocol to adaptively manage the active time of thenodes according to the amount of energy they have harvested. The solution, which is calledEA-MAC, is based on the unslotted CSMA/CA algorithm of IEEE 802.15.4. UnslottedCSMA is a commonly used version of CSMA where no time synchronization exists. If anode has enough energy stored in its capacitor (i.e., above a defined threshold), the nodewakes up to assess the status of the communication channel and transmit a single packet ofdata. The random back-off time integral to the protocol design depends on a number offactors. These include the number of channel assessments, a (constant) maximum back-offtime, and a fairness co-efficient based on the actual and the average energy harvesting rateof all harvesting nodes in the network. However, the authors do not provide detail as tohow the latter is communicated to the nodes. The simulation results show that EA-MACcan achieve high levels of fairness compared to the version without the fairness co-efficient.The authors also present a prototype in an extended work [180].

Naderi et al. [181] present a medium access protocol developed to enable on-demandenergy transfer for energy constrained devices. The protocol is called RF-MAC and itsoperation is divided into three phases during which: (a) A node—whose energy is below athreshold—broadcasts an energy request and one or more transmitters respond. (b) Thenode decides about how much energy needs to be provided by the transmitters based on itsown needs and the information it got from the neighborhood. The transmitters are dividedinto two groups to maximize the energy efficiency through constructive interference. This isachieved by assigning to the transmitters two slightly separated energy transfer frequencies.In the third phase, the protocol decides to give higher priority to the energy requestpackets than the data packets for accessing the channel. In this way, nodes with higherresidual energy have a higher access priority for data communication. It is shown throughsimulations and experiments that the proposed protocol achieves higher data throughputcompared to the conventional CSMA approach.

Nguyen et al. [182] propose a IEEE 802.15.4-based network MAC layer to harvestenergy from ambient LTE signals. The authors assume that the nodes can harvest energyusing broadcast transmissions of the downlink LTE channel. Using the residual energyof the nodes, the QoS requirements, and the broadcasted energy, a centralized solution isproposed to adapt the active time of the nodes. The results presented by the authors showhigh performance gains in terms of energy efficiency. However, a flat RF to DC energyefficiency factor is assumed for all the nodes which is far from reality.

Ha et al. [183] extend the conventional IEEE 802.11e MAC into a harvest-then-transmit-based MAC. It is assumed that the nodes perform transmissions at the same frequencybands with the RF energy emission. Thus, a new medium access method is required toavoid collisions. The authors first present a performance analysis based on a Markovchain model and steady-state probabilities and, at a second stage, determine the maximum

Sensors 2022, 22, 2990 25 of 36

achieved energy harvesting rate. The authors’ aim is, despite the new MAC layer, to havebackward compatibility with the typical WiFi standard. The simulation results show higherenergy harvesting rates when only a limited number of nodes are present. It is seen thatthe energy rate decreases rapidly with higher number of nodes due to the shorter chargingperiods available for each node. However, based on the results presented in the paper,the proposed method seems to perform as well as the time-slotted solution of [184] and isseen to outperform the RF-MAC approach [181] and a distributed opportunistic schedulingscheme [185].

Kim et al. [186] propose a new MAC layer to support power emission of dedicatedchargers over a contention-based channel access network. The authors’ main objectiveis to prevent simultaneous data and power emissions over the same channel that couldpotentially lead to collisions and network disruptions. As a consequence, the design allowsenergy beacons to be transmitted only when a charger finds the channel idle. The presentedsimulation results show an improvement from 20% to 150% in terms of harvested energycompared to RF-MAC [181]. However, as with some of the previous works, the results arebased on ideal path-loss conditions while the RF-to-DC conversion efficiency is not takeninto account.

Unlike the previous CSMA-based approaches, Ju et al. [184] propose a “harvest-then-transmit” approach where a dedicated RF transmitter transfers energy to a set of nodes.The nodes use the uplink transmissions to transmit data packets once they have got enoughenergy from the transmitter. The uplink transmissions follow a TDMA MAC. The authorsdeal with the theoretical background of finding optimal joint allocation time for downlinks(RF energy) and uplinks (data). The analysis reveals a fairness issue for the distant nodesbecause they have to transmit with higher power but, at the same time, they obtain lessenergy from the transmitter. To address this issue, the authors revise their algorithm to findthroughput-fairness trade-offs.

Table 8. Characteristics and features of the proposed RF power harvesting enabled MAC protocols.

Ref. RF Energy EmissionAmbient/DedicatedHarvesting

Medium-AccessMethod (Radio)

MaximizationParameters

ExperimentallyValidated

[179] Constantly Ambient CSMA(IEEE802.15.4)

Throughput,Fairness Yes

[181] On demand Dedicated CSMA Throughput Yes

[182] Constantly Ambient (LTE) CSMA(IEEE802.15.4) Throughput No

[183] Harvest-then-transmit Dedicated (WiFi) CSMA (IEEE802.11) Throughput No

[186] When no data Dedicated CSMA Harvestingenergy No

[184] Harvest-then-transmit Dedicated TDMA Throughput,Fairness No

8. Limitations, Open Issues, and Future Research Directions

The literature covered in the previous sections focuses on a number of tenets relatedto the performance of RF energy harvesting systems and describes recent advancementsin these areas. However, there are numerous technological limitations and open issuesthat need to be tackled or mitigated and are described in the following paragraphs. Fu-ture research directions for many of the topics covered throughout this manuscript aresummarized in Table 9. In addition to the area-specific challenges presented herein, thissection highlights additional challenges of a more general nature which are crucial to theperformance of RF powered systems.

Sensors 2022, 22, 2990 26 of 36

Table 9. Challenges and future research directions in RF power harvesting systems.

Section Challenges and Open Issues Future Directions

Evaluation metrics for RFpower harvesting

– Choice of optimal operational frequency.– Achieving optimal power conversion efficiency.

– Improvement in the sensitivity of the rectennasystem.

– Optimizing the DC output power.

Applications of RF energyharvesting

– Energy optimal IoT operation.– Green and eco-sustainable IoT.– Optimal use of small power magnitudes.– Reducing the operational costs of devices.

– Designing smart wearables for healthcare in-formatics.

– Designing battery free sensors for structuralhealth monitoring.

– Exploiting RF power harvesting for RFID tags.

Dedicated vs. Ambient RFenergy Harvesting

– Small power densities of ambient RF sources.– Higher distances between RF transmitters and

harvesters.– Higher CAPEX in case of dedicated RF power

sources.– Site selection for installing dedicated chargers.– Antenna & Rectifier design for static ambient RF

harvesting

– Investigating the hybrid (ambient & dedi-cated) RF power sources.

– Improving the conversion efficiency of hybridRF harvesting systems.

– Exploiting RF power harvesting for RFID tags.– exploring ways to directly feed ambient RF

energy to sensors.

Energy propagation models

– To measure the characteristic signal parameters.– The accurate prediction of the RF signal behavior

in real time.– Most of theoretical propagating models are not

validated through experiments.

– The analysis of RF signal propagation employ-ing different models.

– Estimating the distribution of RF power con-sidering fading effects and path loss.

– Improving the precision of the RSSI of an RFsignal at harvester side.

Rectenna Architecture forRF energy Harvesters

– Antenna design with maximum power transferefficiency.

– Choice of a best polarization technique for opti-mum power conversion efficiency.

– Selection of best orientation of the antennas foroptimum conversion efficiency.

– Proposing new filtennas offering low pass fil-ters for rejecting the harmonic rejection.

– Balancing the trade-off between single &multi-antenna design in RF harvesters.

– Exploring multi-antenna RF harvesting tech-niques(e.g., beamforming to attain spatialmultiplexing) for higher efficiency.

MAC Protocols for RFpower Harvesting

– Energy fairness among the nodes.– Lack of experimentally validated models.

– Design of MAC protocol to ensure the fairenergy quota across all the nodes.

– RF transmitter localization across the net-work.

8.1. Limitations

The reduction in available energy density in line with the propagation distance is oneof the major limitations to be considered when developing RF power harvesting systems.The inverse square law (i.e., I ∝ (1/d2)) states that the power density of the RF signalsdeclines proportionally to the inverse of the square of the propagation distance betweentransmitter and receiver and 1 W is the maximum transmitter power output in EU regionin compliance with the Federal Communications Commission (FCC) local regulations.In addition, FCC has capped the maximum Effective Isotropic Radiated Power (EIRP)emission up to 4 W equivalent isotropic radiations, whereas [187] indicates that only adistance of 15 m could be supported to achieve an energy transfer rate of 5.5 µW consideringa 4 W power source.

Miniaturizing the sensor design is also of critical significance in the presence of RFharvesting circuits as it should be comparable to the size of a battery powered sensor device.As described in Section 6, the antenna, matching network, and rectifier are the integralcomponents of an RF power harvester and the antenna size has a decisive role in determiningthe harvesting density so it becomes even more pertinent to reduce the size of the devicesembedded to the designed sensors while improving harvesting efficiency. Thus, decreasingthe circuit (sensor) size and simultaneously improving the harvesting efficiency of the circuitare two different goals and it becomes challenging to achieve both at the same time.

Sensors 2022, 22, 2990 27 of 36

The sensitivity of an RF harvesting circuit (i.e., the minimum power required by theharvester to start the harvesting process) also plays a key role in performance optimizationby improving conversion efficiency. There can be a significant difference in the sensitivityof the data receiver component when compared to that of RF power harvester. As aresult, if the receiver is located far away from the transmitter, it may be able to decode theinformation but can not effectively extract the energy from an RF signal from the sametransmitter. Consequently, Simultaneous Wireless Information and Power Transfer (SWIPT)techniques do not perform well and efforts are needed on improving the sensitivity of RFharvesting circuit so as to enhance the conversion efficiency at different distances.

8.2. Open Issues and Mitigation Actions

Impedance mismatch, which can be defined as the phenomenon caused due to thedifference of the input resistance and reactance between the rectifier and the antenna, isalso an important concern in RF power harvesters. Consequently, the receiver antenna isunable to transfer all of its harvested power to the rectifier that is one of the most commoncauses of degradation in power conversion efficiency. Hence, the research communityshould focus on developing some efficient and smart circuit design techniques to effectivelyminimize/mitigate this impedance variation and develop system level design activities forantenna and rectenna, incorporating not just the design of the antenna but also consideringthe impedance characteristics of the antenna.

Energy loss in a non-line of sight environment is another issue that deserves attention.A substantial amount of energy loss is anticipated coming from a source to a harvester inthis case. So, if the transmitter (i.e., the energy source) is to provide energy to multiplereceivers at the same time, line of sight at appropriate distance should be maintained.This becomes particularly relevant for sensors operating in a mobile environment wherethe mobility may severely impact the harvestable power densities when transmittersand/or harvesters are mobile. Reconfigurable intelligent surfaces could help in such anenvironment through controlling the propagation of electromagnetic signals by changingthe electric and magnetic properties of the surface aiming to minimize such losses [188].

A scheduling policy is an important aspect to consider in RF power harvesting sys-tems as it can turn potential EM interference into useful energy by introducing an efficientscheduling policy. Spectrum scheduling could be useful to avoid or mitigate the interfer-ence by employing some protocol management techniques such as interference cancellationand alignment of interfering signals. Here, simultaneously mitigating the interference andtransferring the energy from transmitter to receiver might be two contradictory goals tobe achieved in RF power harvesting that could indeed be challenging as the interferencemitigation techniques could impact the energy harvesting magnitudes. Such power man-agement techniques along with an appropriate scheduling policy can result in a significantimprovement in energy efficiency.

Finally, an evident issue in the domain of RF-harvesting is that of energy fairness amongthe nodes. This means that nodes that are distant from the power transmitters receive not onlyless energy but they also have less chances to compete for the data channel. Even though someof the studies try to resolve this issue by adapting the duty cycle time of the nodes, the problemstill remains. The issue could potentially be tackled or mitigated by appropriately placingthe transmitters in the spatial domain [189–191]. Another issue with most of the proposedMAC protocols is the lack of experimentation and reliance on simulations to characterizeenergy harvesting networks. Typically results are based on mathematical analysis, computersimulations and modeling—usually assuming free-space path loss—while no real energyconversion efficiency is evaluated in testbeds and physical deployments.

8.3. Other Research Directions

There are a number of challenges that led future directions and opportunities inthis domain.

Sensors 2022, 22, 2990 28 of 36

The potential amount of harvested energy available from RF power harvesting makesthe EM spectrum potentially a valuable resource and opens up opportunities in establishinga competitive market to economically manage and exploit this resource. For instance,wireless charging service providers could be seen as small RF power suppliers contributingtowards (partially) meeting the energy demands of nodes over the network. As such, theseservice providers can decide on pricing of the commodity they provide while guaranteeingthe quality of service for the sensor networks of their customers.

Mobility is another key concern in RF power harvesting and information transferwhere network sources, sensor nodes, and gateways may all be mobile in nature. Thisissue becomes even more critical in the case of the time varying nature of RF harvestingand information transmission as the amount of energy harvested is not always the samein magnitude and power density. Appropriate resource allocation can be tricky to beimplement in these kinds of systems where the research opportunities exist in achievingdynamic and adaptive resource allocation.

Network coding schemes are another potential topic of research as such schemes canheavily influence the energy efficiency of a wireless system as it enables wireless trans-mitters to transmit data, information, and energy simultaneously over common frequencybands. This characteristic enables an improvement in RF energy harvesting densities,particularly in case of large-scale networked systems. In such systems, data transmitterand relay nodes can effectively use available time slots for power harvesting when they areidle from a data transmission perspective. The authors [192] suggest that network lifetimegain has the potential to be improved by up to 70% when employing RF power harvestingtechnology with appropriate coding schemes. A diverse range of network model andcoding schemes need to be explored in order to improve the network lifetime. It is still anopen issue to investigate whether the RF power harvesting improves the upper bound ofenergy gains and to what extent.

Finally, it has widely been acknowledged that RF exposure causes heating somematerials with finite conductivity [193]. Several studies [194–198] have been conductedon the impact on human health of electromagnetic radiations from mobile devices andcellular infrastructures. The majority of these studies identify EM radio waves as non-harmful for human health. A minority of them [197,198] report some effects on genes whenexceeding the upper bound of internationally recognized standards on secure power levels.The impact of RF chargers used for the energy harvesting technologies discussed in thispublication on health as these may emit radio waves at high power densities so there is aneed to gauge the safety standards while deploying these RF chargers in conjunction withFCC standards.

9. Conclusions

With the advent of IoT, exploring different methods of harnessing energy from theenvironment (such as electromagnetic, photoelectric, thermoelectric, and piezoelectric toname a few) in which sensor nodes are deployed to feed the end devices, has globallybecome a topic of research interest. All of them have their own sets of pros and cons thatdetermine their suitability and limitations for a unique use case based on their differentcharacteristics and design considerations. RF energy harvesting is one promising solutionto achieve network longevity where a sufficient amount of energy could be harvested fromboth ambient and dedicated RF sources. However, the technological advancements in thisarea (specifically, harvesting techniques, energy propagation models, rectenna architectures,and MAC protocol design) are progressing slowly and only a limited number of real proto-types have been reported and a number of interesting research challenges are identified.The paper comprehensively surveys the current state of the art in the domain of energyharvesting with a particular focus on RF energy harvesting. The factors that influencethe performance of RF energy harvesting systems and a range of harvesting applications(e.g., industrial automation, healthcare informatics, structural health monitoring amongmany others) are described. The review covers the disparate research activities required

Sensors 2022, 22, 2990 29 of 36

for RF energy harvesting including dedicated vs. ambient harvesting sources, evaluationmetrics, energy propagation models, rectenna architectures, and MAC protocols for RFpower harvesting. There exist a number of open research challenges and future researchdirections for the new entrants working into RF energy harvesting domain.

Funding: This project is co-funded by the European Regional Development Fund (ERDF) underIreland’s European Structural and Investment Funds Programmes 2014-2020. This publication hasemanated from research conducted with the financial support of Science Foundation Ireland (SFI)in conjunction with Johnson and Johnson under Grant Number [16/RC/3918] CONFIRM which isco-funded under the ERDF.

Acknowledgments: This project is co-funded by the European Regional Development Fund (ERDF)under Ireland’s European Structural and Investment Funds Programmes 2014–2020, with the financialsupport of Science Foundation Ireland under Grant number 16/RC/3918-CONFIRM in conjunctionwith Johnson & Johnson. Aspects of this publication were supported by Grant number 13/RC/2077-CONNECT and Enterprise Ireland which are co-funded under the ERDF.

Conflicts of Interest: The authors declare no conflict of interest.

References1. Gubbi, J.; Buyya, R.; Marusic, S.; Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions.

Future Gener. Comput. Syst. 2013, 29, 1645–1660. [CrossRef]2. VXCHNGE. Comprehensive Guide to IoT Statistics. Available online: https://www.vxchnge.com/blog/iot-statistics (accessed

on 14 February 2021).3. Palattella, M.R.; Accettura, N.; Vilajosana, X.; Watteyne, T.; Grieco, L.A.; Boggia, G.; Dohler, M. Standardized protocol stack for

the internet of (important) things. IEEE Commun. Surv. Tutor. 2012, 15, 1389–1406. [CrossRef]4. Sanchez, D.O.M. Sustainable Development Challenges and Risks of Industry 4.0: A literature review. In Proceedings of the 2019

Global IoT Summit (GIoTS), Aarhus, Denmark, 17–21 June 2019; pp. 1–6.5. Luthra, S.; Mangla, S.K. Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies.

Process Saf. Environ. Prot. 2018, 117, 168–179. [CrossRef]6. Elijah, O.; Rahman, T.A.; Orikumhi, I.; Leow, C.Y.; Hindia, M.N. An overview of Internet of Things (IoT) and data analytics in

agriculture: Benefits and challenges. IEEE Internet Things J. 2018, 5, 3758–3773. [CrossRef]7. Stojkoska, B.L.R.; Trivodaliev, K.V. A review of Internet of Things for smart home: Challenges and solutions. J. Clean. Prod. 2017,

140, 1454–1464. [CrossRef]8. Silva, B.N.; Khan, M.; Han, K. Towards sustainable smart cities: A review of trends, architectures, components, and open

challenges in smart cities. Sustain. Cities Soc. 2018, 38, 697–713. [CrossRef]9. Sherazi, H.H.R.; Grieco, L.A.; Boggia, G.; Imran, M.A. Cost Efficiency Optimization for Industrial Automation. In Wireless

Automation as an Enabler for the Next Industrial Revolution; Wiley: Hoboken, NJ, USA, 2020; pp. 141–171, ISBN 9781119552635.10. Institute of Mechanical Engineers. Engineering News. 2021. Available online: https://www.vox.com/energy-and-environment

/2018/4/27/17283830/batteries-energy-storage-carbon-emissions (accessed on 27 March 2021).11. Benedek, J.; Sebestyén, T.T.; Bartók, B. Evaluation of renewable energy sources in peripheral areas and renewable energy-based

rural development. Renew. Sustain. Energy Rev. 2018, 90, 516–535. [CrossRef]12. Jurasz, J.; Canales, F.; Kies, A.; Guezgouz, M.; Beluco, A. A review on the complementarity of renewable energy sources: Concept,

metrics, application and future research directions. Sol. Energy 2020, 195, 703–724. [CrossRef]13. De Mil, P.; Jooris, B.; Tytgat, L.; Catteeuw, R.; Moerman, I.; Demeester, P.; Kamerman, A. Design and implementation of a

generic energy-harvesting framework applied to the evaluation of a large-scale electronic shelf-labeling wireless sensor network.EURASIP J. Wirel. Commun. Netw. 2010, 2010, 343690. [CrossRef]

14. Code, S. Limits of Human Exposure to Radiofrequency Electromagnetic Fields in the Frequency Range from 3 kHz to 300 GHz; Environ-mental Health Directorate, Health Protection Branch, Health Canada: Ottawa, ON, Canada, 2015.

15. Powercast Corporation. TX91501b POWERCASTER® TRANSMITTER. Available online: https://www.powercastco.com/products/powercaster-transmitter (accessed on 4 January 2021).

16. Powercast Corporation. POWERCASTER® POWERSPOT. Available online: https://www.powercastco.com/products/powerspot (accessed on 4 January 2021).

17. Ossia. Ossia’s Cota: Real Wireless Power. Available online: https://www.ossia.com/cota (accessed on 4 January 2021).18. Ren, J.; Hu, J.; Zhang, D.; Guo, H.; Zhang, Y.; Shen, X. RF energy harvesting and transfer in cognitive radio sensor networks:

Opportunities and challenges. IEEE Commun. Mag. 2018, 56, 104–110. [CrossRef]19. Verma, G.; Sharma, V. A survey on hardware design issues in RF energy harvesting for wireless sensor networks (WSN). In

Proceedings of the 2016 5th International Conference on Wireless Networks and Embedded Systems (WECON), Rajpura, India,14–16 October 2016; pp. 1–9.

Sensors 2022, 22, 2990 30 of 36

20. Mukminin, M.K.; Nurhayati, N.; Rakhmawati, L.; Baskoro, F. Literature Study of Harvesting Energy with Resources RadioFrequency. INAJEEE (Indones. J. Electr. Electron. Eng.) 2020, 3, 19–26. [CrossRef]

21. Lu, X.; Wang, P.; Niyato, D.; Kim, D.I.; Han, Z. Wireless networks with RF energy harvesting: A contemporary survey. IEEECommun. Surv. Tutor. 2014, 17, 757–789. [CrossRef]

22. Nintanavongsa, P. A survey on RF energy harvesting: Circuits and protocols. Energy Procedia 2014, 56, 414–422. [CrossRef]23. Sidhu, R.K.; Ubhi, J.S.; Aggarwal, A. A survey study of different RF energy sources for RF energy harvesting. In Proceedings of

the 2019 International Conference on Automation, Computational and Technology Management (ICACTM), London, UK, 24–26April 2019; pp. 530–533.

24. Soyata, T.; Copeland, L.; Heinzelman, W. RF energy harvesting for embedded systems: A survey of tradeoffs and methodology.IEEE Circuits Syst. Mag. 2016, 16, 22–57. [CrossRef]

25. Tran, L.G.; Cha, H.K.; Park, W.T. RF power harvesting: A review on designing methodologies and applications. Micro Nano Syst.Lett. 2017, 5, 14. [CrossRef]

26. Srinivasu, G.; Sharma, V.; Anveshkumar, N. A Survey on Conceptualization of RF Energy Harvesting. J. Appl. Sci. Comput.(JASC) 2019, 6, 791–800.

27. Stoopman, M.; Keyrouz, S.; Visser, H.; Philips, K.; Serdijn, W. A self-calibrating RF energy harvester generating 1V at −26.3 dBm.In Proceedings of the 2013 Symposium on VLSI Circuits, Kyoto, Japan, 12–14 June 2013; pp. C226–C227.

28. Stoopman, M.; Keyrouz, S.; Visser, H.J.; Philips, K.; Serdijn, W.A. Co-design of a CMOS rectifier and small loop antenna for highlysensitive RF energy harvesters. IEEE J. Solid-State Circuits 2014, 49, 622–634. [CrossRef]

29. Sample, A.P.; Parks, A.N.; Southwood, S.; Smith, J.R. Wireless ambient radio power. In Wirelessly Powered Sensor Networks andComputational RFID; Springer: Berlin/Heidelberg, Germany, 2013; pp. 223–234.

30. Papotto, G.; Carrara, F.; Finocchiaro, A.; Palmisano, G. A 90-nm CMOS 5-Mbps crystal-less RF-powered transceiver for wirelesssensor network nodes. IEEE J. -Solid-State Circuits 2013, 49, 335–346. [CrossRef]

31. Clerckx, B.; Popovic, Z.; Murch, R. Future Networks With Wireless Power Transfer and Energy Harvesting. Proc. IEEE 2022,110, 3–7. [CrossRef]

32. Clerckx, B.; Kim, J.; Choi, K.W.; Kim, D.I. Foundations of Wireless Information and Power Transfer: Theory, Prototypes, andExperiments. Proc. IEEE 2022, 110, 8–30. [CrossRef]

33. Gu, X.; Hemour, S.; Wu, K. Far-Field Wireless Power Harvesting: Nonlinear Modeling, Rectenna Design, and EmergingApplications. Proc. IEEE 2022, 110, 56–73. [CrossRef]

34. Kwiatkowski, E.; Estrada, J.A.; López-Yela, A.; Popovic, Z. Broadband RF Energy-Harvesting Arrays. Proc. IEEE 2022, 110, 74–88.[CrossRef]

35. Zhou, J.; Zhang, P.; Han, J.; Li, L.; Huang, Y. Metamaterials and Metasurfaces for Wireless Power Transfer and Energy Harvesting.Proc. IEEE 2022, 110, 31–55. [CrossRef]

36. Wu, Q.; Guan, X.; Zhang, R. Intelligent Reflecting Surface-Aided Wireless Energy and Information Transmission: An Overview.Proc. IEEE 2022, 110, 150–170. [CrossRef]

37. Moore, G.E.; Rosenthal, J.D.; Smith, J.R.; Reynolds, M.S. Adaptive Wireless Power Transfer and Backscatter Communication forPerpetual Operation of Wireless Brain–Computer Interfaces. Proc. IEEE 2022, 110, 89–106. [CrossRef]

38. Song, C.; Ding, Y.; Eid, A.; Hester, J.G.D.; He, X.; Bahr, R.; Georgiadis, A.; Goussetis, G.; Tentzeris, M.M. Advances in WirelesslyPowered Backscatter Communications: From Antenna/RF Circuitry Design to Printed Flexible Electronics. Proc. IEEE 2022,110, 171–192. [CrossRef]

39. Wei, Z.; Yu, X.; Ng, D.W.K.; Schober, R. Resource Allocation for Simultaneous Wireless Information and Power Transfer Systems:A Tutorial Overview. Proc. IEEE 2022, 110, 127–149. [CrossRef]

40. Psomas, C.; You, M.; Liang, K.; Zheng, G.; Krikidis, I. Design and Analysis of SWIPT With Safety Constraints. Proc. IEEE 2022,110, 107–126. [CrossRef]

41. Lu, X.; Cong Luong, N.; Hoang, D.T.; Niyato, D.; Xiao, Y.; Wang, P. Secure Wirelessly Powered Networks at the Physical Layer:Challenges, Countermeasures, and Road Ahead. Proc. IEEE 2022, 110, 193–209. [CrossRef]

42. Frost & Sullivan. What’s Hot in the Industrial Automation and Process Control Industry. 2012. Available online: https://pdfslide.net/business/frost-sullivan-whats-hot-in-the-industrial-automation-and-process-control-industry.html (accessed on21 October 2020).

43. Asghari, P.; Rahmani, A.M.; Javadi, H.H.S. Internet of Things applications: A systematic review. Comput. Netw. 2019, 148, 241–261.[CrossRef]

44. Choudhary, P.; Bhargava, L.; Singh, V.; Choudhary, M.; Kumar Suhag, A. A survey–Energy harvesting sources and techniques forinternet of things devices. Mater. Today Proc. 2020, 30, 52–56. [CrossRef]

45. Alsharif, M.H.; Kim, S.; Kuruoglu, N. Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identifica-tion: A Review. Symmetry 2019, 11, 865. [CrossRef]

46. Adila, A.S.; Husam, A.; Husi, G. Towards the self-powered Internet of Things (IoT) by energy harvesting: Trends and technologiesfor green IoT. In Proceedings of the 2018 2nd International Symposium on Small-Scale Intelligent Manufacturing Systems (SIMS),Cavan, Ireland, 16–18 April 2018; pp. 1–5. [CrossRef]

47. Krupitzer, C.; Müller, S.; Lesch, V.; Züfle, M.; Edinger, J.; Lemken, A.; Schäfer, D.; Kounev, S.; Becker, C. A Survey on HumanMachine Interaction in Industry 4.0. arXiv 2020, arXiv:2002.01025.

Sensors 2022, 22, 2990 31 of 36

48. Sherazi, H.H.R.; Grieco, L.A.; Imran, M.A.; Boggia, G. Energy-efficient LoRaWAN for Industry 4.0 Applications. IEEE Trans. Ind.Inform. 2020, 17, 891–902. [CrossRef]

49. Tahir, M.A.; Ferrer, B.R.; Luis, J.; Lastra, M. An Approach for Managing Manufacturing Assets through Radio Frequency EnergyHarvesting. Sensors 2019, 19, 438. [CrossRef] [PubMed]

50. Tang, X.; Wang, X.; Cattley, R.; Gu, F.; Ball, A.D. Energy harvesting technologies for achieving self-powered wireless sensornetworks in machine condition monitoring: A review. Sensors 2018, 18, 4113. [CrossRef] [PubMed]

51. Shuhaiber, A.; Mashal, I. Understanding users’ acceptance of smart homes. Technol. Soc. 2019, 58, 101110. [CrossRef]52. Baker, S.B.; Xiang, W.; Atkinson, I. Internet of things for smart healthcare: Technologies, challenges, and opportunities. IEEE

Access 2017, 5, 26521–26544. [CrossRef]53. Sumalee, A.; Ho, H.W. Smarter and more connected: Future intelligent transportation system. IATSS Res. 2018, 42, 67–71.

[CrossRef]54. Cheikhrouhou, O.; Koubâa, A.; Zarrad, A. A Cloud Based Disaster Management System. J. Sens. Actuator Netw. 2020, 9, 6.

[CrossRef]55. Prati, A.; Shan, C.; Wang, K.I.K. Sensors, vision and networks: From video surveillance to activity recognition and health

monitoring. J. Ambient. Intell. Smart Environ. 2019, 11, 5–22.56. Zungeru, A.M.; Ang, L.M.; Prabaharan, S.; Seng, K.P. Radio frequency energy harvesting and management for wireless sensor

networks. In Green Mobile Devices and Networks: Energy Optimization and Scavenging Techniques; Number 13 in 0; CRC Press: NewYork, NY, USA, 2012; pp. 341–368.

57. Visser, H.J.; Vullers, R.J.M. RF Energy Harvesting and Transport for Wireless Sensor Network Applications: Principles andRequirements. Proc. IEEE 2013, 101, 1410–1423. [CrossRef]

58. Boisseau, S.; Despesse, G. Energy harvesting, wireless sensor networks & opportunities for industrial applications. EE Times, 2012.59. Nintanavongsa, P.; Muncuk, U.; Lewis, D.R.; Chowdhury, K.R. Design optimization and implementation for RF energy harvesting

circuits. IEEE J. Emerg. Sel. Top. Circuits Syst. 2012, 2, 24–33. [CrossRef]60. Sample, A.; Smith, J.R. Experimental results with two wireless power transfer systems. In Proceedings of the 2009 IEEE Radio

and Wireless Symposium, San Diego, CA, USA, 18–22 January 2009; pp. 16–18.61. Iyengar, A.; Kundu, A.; Pallis, G. Healthcare Informatics and Privacy. IEEE Internet Comput. 2018, 22, 29–31. [CrossRef]62. Yang, L.; Zhou, Y.J.; Zhang, C.; Yang, X.M.; Yang, X.X.; Tan, C. Compact multiband wireless energy harvesting based battery-free

body area networks sensor for mobile healthcare. IEEE J. Electromagn. Microwaves Med. Biol. 2018, 2, 109–115. [CrossRef]63. Anwar, M.; Abdullah, A.H.; Qureshi, K.N.; Majid, A.H. Wireless body area networks for healthcare applications: An overview.

Telkomnika 2017, 15, 1088–1095. [CrossRef]64. Luo, Y.; Pu, L.; Zhao, Y. RF Energy Harvesting Sensor Networks for Healthcare of Animals: Opportunities and Challenges. arXiv

2018, arXiv:1803.00106.65. Saraereh, O.A.; Alsaraira, A.; Khan, I.; Choi, B.J. A hybrid energy harvesting design for on-body Internet-of-Things (IoT) networks.

Sensors 2020, 20, 407. [CrossRef]66. Haghi, M.; Thurow, K.; Stoll, R. Wearable devices in medical internet of things: Scientific research and commercially available

devices. Healthc. Inform. Res. 2017, 23, 4–15. [CrossRef]67. Borges, L.M.; Chávez-Santiago, R.; Barroca, N.; Velez, F.J.; Balasingham, I. Radio-frequency energy harvesting for wearable

sensors. Healthc. Technol. Lett. 2015, 2, 22–27. [CrossRef]68. Lin, C.; Chiu, C.; Gong, J. A Wearable Rectenna to Harvest Low-Power RF Energy for Wireless Healthcare Applications.

In Proceedings of the 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics(CISP-BMEI), Beijing, China, 13–15 October 2018; pp. 1–5. [CrossRef]

69. Ren, H.; Meng, M.H.; Chen, X. Physiological information acquisition through wireless biomedical sensor networks. In Proceedingsof the 2005 IEEE International Conference on Information Acquisition, Hong Kong, China, 27 June–3 July 2005; p. 6.

70. Jia, X.; Feng, Q.; Fan, T.; Lei, Q. RFID technology and its applications in Internet of Things (IoT). In Proceedings of the 2012 2ndInternational Conference on Consumer Electronics, Communications and Networks (CECNet), Yichang, China, 21–23 April 2012;pp. 1282–1285.

71. Mhatre, P.; Duche, R.; Nawale, S.; Patil, P. RF power harvesting system for RFID applications in multiband systems. In Proceedingsof the 2015 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Denton, TX,USA, 13–15 July 2015; pp. 1–5. [CrossRef]

72. Hande, A.; Bridgelall, R.; Bhatia, D. Energy harvesting for active RF sensors and ID tags. In Energy Harvesting Technologies;Springer: Berlin/Heidelberg, Germany, 2009; pp. 459–492.

73. Aparicio, M.P.; Bakkali, A.; Pelegri-Sebastia, J.; Sogorb, T.; Bou, V. Radio frequency energy harvesting-sources and techniques. InRenewable Energy: Utilisation and System Integration; Intechopen: London, UK, 2016.

74. Cui, L.; Zhang, Z.; Gao, N.; Meng, Z.; Li, Z. Radio frequency identification and sensing techniques and their applications—Areview of the state-of-the-art. Sensors 2019, 19, 4012. [CrossRef] [PubMed]

75. Olgun, U.; Chen, C.; Volakis, J.L. Wireless power harvesting with planar rectennas for 2.45 GHz RFIDs. In Proceedingsof the 2010 URSI International Symposium on Electromagnetic Theory, Berlin, Germany, 16–19 August 2010; pp. 329–331.https://doi.org10.1109/URSI-EMTS.2010.5637008.

Sensors 2022, 22, 2990 32 of 36

76. Pellerano, S.; Alvarado, J.; Palaskas, Y. A mm-Wave Power-Harvesting RFID Tag in 90 nm CMOS. IEEE J. Solid-State Circuits 2010,45, 1627–1637. [CrossRef]

77. Bakhtiar, A.S.; Jalali, M.S.; Mirabbasi, S. An RF power harvesting system with input-tuning for long-range RFID tags. InProceedings of 2010 IEEE International Symposium on Circuits and Systems, Paris, France, 30 May–2 June 2010; pp. 4085–4088.[CrossRef]

78. Slesinski, R.J. Power Harvesting for Actively Powered RFID Tags and Other Electronic Sensors. U.S. Patent App. 12/039,691,3 September 2009

79. Sony, S.; Laventure, S.; Sadhu, A. A literature review of next-generation smart sensing technology in structural health monitoring.Struct. Control. Health Monit. 2019, 26, e2321. [CrossRef]

80. Srinivasan, R.; Ali, U.H.H. Energy harvesting wireless sensor for achieving self-powered structural health monitoring system.Circuit World 2020, 46, 307–315. [CrossRef]

81. Loubet, G.; Takacs, A.; Gardner, E.; De Luca, A.; Udrea, F.; Dragomirescu, D. LoRaWAN Battery-Free Wireless Sensors NetworkDesigned for Structural Health Monitoring in the Construction Domain. Sensors 2019, 19, 1510. [CrossRef]

82. Loubet, G.; Takacs, A.; Dragomirescu, D. Implementation of a battery-free wireless sensor for cyber-physical systems dedicatedto structural health monitoring applications. IEEE Access 2019, 7, 24679–24690. [CrossRef]

83. Sidibe, A.; Takacs, A.; Okba, A.; Aubert, H. Design and Characterization of a Compact Rectenna for Structural Health MonitoringApplications. In Proceedings of the 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI RadioScience Meeting, Atlanta, GA, USA, 7–12 July 2019; pp. 1803–1804. [CrossRef]

84. Cao, S.; Li, J. A survey on ambient energy sources and harvesting methods for structural health monitoring applications. Adv.Mech. Eng. 2017, 9, 1687814017696210. [CrossRef]

85. Federal Communications Commission (FCC). Available online: https://www.fcc.gov (accessed on 21 October 2020).86. Zorbas, D.; Raveneau, P.; Ghamri-Doudane, Y. Assessing the cost of RF-power harvesting nodes in wireless sensor networks. In

Proceedings of the 2016 IEEE Global Communications Conference, GLOBECOM 2016—Proceedings, Washington, DC, USA, 4–8December 2016. [CrossRef]

87. Radhika, N.; Tandon, P.; Prabhakar, T.V.; Vinoy, K.J. RF Energy Harvesting For Self Powered Sensor Platform. In Proceedings ofthe 2018 16th IEEE International New Circuits and Systems Conference (NEWCAS), Montreal, QC, Canada, 24–27 June 2018;pp. 148–151. [CrossRef]

88. Zhou, R.; Cheng, R.S. Optimal Charge Scheduling for Energy-Constrained Wireless-Powered Network. In Proceedings of the2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 15–18 April 2019; pp. 612–615.

89. Lakshmi, P.S.; Jibukumar, M.G.; Neenu, V.S. Network lifetime enhancement of multi-hop wireless sensor network by RF energyharvesting. In Proceedings of the International Conference on Information Networking, Chiang Mai, Thailand, 10–12 January2018; pp. 738–743. [CrossRef]

90. Li, Q.; Gao, J.; Liang, H.; Zhao, L.; Tang, X. Optimal power allocation for wireless sensor powered by dedicated RF energy source.IEEE Trans. Veh. Technol. 2019, 68, 2791–2801. [CrossRef]

91. Erol-Kantarci, M.; Mouftah, H.T. Suresense: Sustainable wireless rechargeable sensor networks for the smart grid. IEEE Wirel.Commun. 2012, 19, 30–36. [CrossRef]

92. Erol-Kantarci, M.; Mouftah, H.T. Mission-aware placement of RF-based power transmitters in wireless sensor networks. InProceedings of the 2012 IEEE Symposium on Computers and Communications (ISCC), Cappadocia, Turkey, 1–4 July 2012;pp. 12–17.

93. Erol-Kantarci, M.; Mouftah, H.T. DRIFT: Differentiated RF power transmission for wireless sensor network deployment in thesmart grid. In Proceedings of the 2012 IEEE Globecom Workshops, Anaheim, CA, USA, 3–7 December 2012; pp. 1491–1495.

94. Gu, X.; Grauwin, L.; Dousset, D.; Hemour, S.; Wu, K. Dynamic ambient RF energy density measurements of Montreal forbattery-free IoT sensor network planning. IEEE Internet Things J. 2021, 8, 13209–13221. [CrossRef]

95. Piñuela, M.; Mitcheson, P.D.; Lucyszyn, S. Ambient RF energy harvesting in urban and semi-urban environments. IEEE Trans.Microw. Theory Tech. 2013, 61, 2715–2726. [CrossRef]

96. Kitazawa, S.; Ban, H.; Kobayashi, K. Energy harvesting from ambient RF sources. In Proceedings of the 2012 IEEE MTT-SInternational Microwave Workshop Series on Innovative Wireless Power Transmission: Technologies, Systems, and Applications,Kyoto, Japan, 10–11 May 2012; pp. 39–42.

97. Bouchouicha, D.; Dupont, F.; Latrach, M.; Ventura, L. Ambient RF energy harvesting. In Proceedings of the InternationalConference on Renewable Energies and Power Quality, Granada, Spain, 23–25 March 2010; Volume 13; pp. 2–6.

98. Kim, S.; Vyas, R.; Bito, J.; Niotaki, K.; Collado, A.; Georgiadis, A.; Tentzeris, M.M. Ambient RF Energy-Harvesting Technologies forSelf-Sustainable Standalone Wireless Sensor Platforms. Proc. IEEE 2014, 102, 1649–1666. [CrossRef]

99. Lorenz, C.H.P.; Hemour, S.; Wu, K. Physical Mechanism and Theoretical Foundation of Ambient RF Power Harvesting UsingZero-Bias Diodes. IEEE Trans. Microw. Theory Tech. 2016, 64, 2146–2158. [CrossRef]

100. Ungan, T.; Reindl, L.M. Harvesting Low Ambient RF-Sources for Autonomous Measurement Systems. In Proceedings of the 2008IEEE Instrumentation and Measurement Technology Conference, Victoria, BC, Canada, 12–15 May 2008; pp. 62–65. [CrossRef]

101. Flint, I.; Lu, X.; Privault, N.; Niyato, D.; Wang, P. Performance analysis of ambient RF energy harvesting: A stochastic geometryapproach. In Proceedings of the 2014 IEEE Global Communications Conference, Austin, TX, USA, 8–12 December 2014;pp. 1448–1453.

Sensors 2022, 22, 2990 33 of 36

102. Lee, S.; Zhang, R.; Huang, K. Opportunistic wireless energy harvesting in cognitive radio networks. IEEE Trans. Wirel. Commun.2013, 12, 4788–4799. [CrossRef]

103. Saeed, W.; Shoaib, N.; Cheema, H.M.; Khan, M.U. RF energy harvesting for ubiquitous, zero power wireless sensors. Int. J.Antennas Propag. 2018, 2018, 8903139. [CrossRef]

104. Tran, H.; Åkerberg, J.; Björkman, M.; Tran, H.V. RF energy harvesting: An analysis of wireless sensor networks for reliablecommunication. Wirel. Netw. 2019, 25, 185–199. [CrossRef]

105. Lee, T.H. The design of cmos radio-frequency integrated circuits. Commun. Eng. 2004, 2, 47.106. Sarkar, T.K.; Ji, Z.; Kim, K.; Medouri, A.; Salazar-Palma, M. A survey of various propagation models for mobile communication.

IEEE Antennas Propag. Mag. 2003, 45, 51–82. [CrossRef]107. Balanis, C.A. Antenna Theory: Analysis and Design; John Wiley & Sons: Hoboken, NJ, USA, 2016.108. Zheng, J.; Zhang, J.; Chen, S.; Zhao, H.; Ai, B. Wireless powered UAV relay communications over fluctuating two-ray fading

channels. Phys. Commun. 2019, 35, 100724. [CrossRef]109. Rappaport, T.S. Wireless Communications: Principles and Practice; Prentice Hall PTR New Jersey: Hoboken, NJ, USA, 1996; Volume 2.110. Oliveira, D.; Oliveira, R. Modeling energy availability in RF Energy Harvesting Networks. In Proceedings of the 2016 International

Symposium on Wireless Communication Systems (ISWCS), Poznan, Poland, 20–23 September 2016; pp. 383–387. [CrossRef]111. Castro, I.T.; Landesa, L.; Serna, A. Modeling the energy harvested by an RF energy harvesting system using gamma processes.

Math. Probl. Eng. 2019, 2019, 8763580. [CrossRef]112. Oliveira, D.; Oliveira, R. Characterization of energy availability in RF energy harvesting networks. Math. Probl. Eng. 2016,

2016, 7849175. [CrossRef]113. Zeng, M.; Li, Z.; Andrenko, A.S.; Zeng, Y.; Tan, H.Z. A compact dual-band rectenna for GSM900 and GSM1800 energy harvesting.

Int. J. Antennas Propag. 2018, 2018, 4781465. [CrossRef]114. Ullah, M.A.; Keshavarz, R.; Abolhasan, M.; Lipman, J.; Esselle, K.P.; Shariati, N. A Review on Antenna Technologies for Ambient

RF Energy Harvesting and Wireless Power Transfer: Designs, Challenges and Applications. IEEE Access 2022, 10, 17231–17267.[CrossRef]

115. Best, S.R. Bandwidth and the lower bound on Q for small wideband antennas. In Proceedings of the 2006 IEEE Antennas andPropagation Society International Symposium, Albuquerque, NM, USA, 9–14 July 2006; pp. 647–650.

116. Microstrip. Microstrip Patch Antenna. Available online: https://www.antenna-theory.com/antennas/patches/antenna.php(accessed on 21 November 2020).

117. Divakaran, S.K.; Krishna, D.D. RF energy harvesting systems: An overview and design issues. Int. J. Microw. Comput.-Aided Eng.2019, 29, e21633. [CrossRef]

118. Shrestha, S.; Noh, S.K.; Choi, D.Y. Comparative study of antenna designs for RF energy harvesting. Int. J. Antennas Propag. 2013,2013, 385260. [CrossRef]

119. Sahal, M.; Tiwari, V. Review of circular polarization techniques for design of microstrip patch antenna. In Proceedings of theInternational Conference on Recent Cognizance in Wireless Communication & Image Processing; Springer: New Delhi, India, 29 April2016; pp. 663–669.

120. Heikkinen, J.; Kivikoski, M. Low-profile circularly polarized rectifying antenna for wireless power transmission at 5.8 GHz. IEEEMicrow. Wirel. Compon. Lett. 2004, 14, 162–164. [CrossRef]

121. Allane, D.; Vera, G.A.; Duroc, Y.; Touhami, R.; Tedjini, S. Harmonic power harvesting system for passive RFID sensor tags. IEEETrans. Microw. Theory Tech. 2016, 64, 2347–2356. [CrossRef]

122. Mishra, S.R.; Kochuthundil Lalitha, S. Filtennas for wireless application: A review. Int. J. Microw. Comput.-Aided Eng. 2019,29, e21879. [CrossRef]

123. Tang, M.; Wang, Y.; Chen, Y. Design of Compact Filtennas with Enhanced Bandwidth. In Proceedings of the 2018 IEEEInternational Conference on Signal Processing, Communications and Computing (ICSPCC), Qingdao, China, 14–16 September2018; pp. 1–4. [CrossRef]

124. Alves, A.A.; da Silva, L.G.; Vilas Boas, E.C.; Spadoti, D.H.; Arismar Cerqueira, S. Continuously frequency-tunable horn filtennasbased on dual-post resonators. Int. J. Antennas Propag. 2019, 2019, 6529343. [CrossRef]

125. Silva, L.; Alves, A.; Cerqueira Sodré, A. Optically controlled reconfigurable filtenna. Int. J. Antennas Propag. 2016, 2016, 7161070.[CrossRef]

126. Razavi, B. The harmonic-rejection mixer [a circuit for all seasons]. IEEE Solid-State Circuits Mag. 2018, 10, 10–14. [CrossRef]127. Forbes, T.; Ho, W.; Gharpurey, R. Design and Analysis of Harmonic Rejection Mixers With Programmable LO Frequency. IEEE J.

Solid-State Circuits 2013, 48, 2363–2374. [CrossRef]128. Rafi, A.A. Harmonic Rejection Mixers for Wideband Receivers. Ph.D. Thesis, The University of Texas at Austin, Austin, TX,

USA, 2013.129. Yeo, J.; Kim, D. Harmonic suppression characteristic of a CPW-FED circular slot antenna using single slot on a ground conductor.

Prog. Electromagn. Res. 2009, 11, 11–19. [CrossRef]130. Shao, S.; Gudan, K.; Hull, J.J. A mechanically beam-steered phased array antenna for power-harvesting applications [antenna

applications corner]. IEEE Antennas Propag. Mag. 2016, 58, 58–64. [CrossRef]131. Ojaroudi Parchin, N.; Jahanbakhsh Basherlou, H.; Al-Yasir, Y.I.; M Abdulkhaleq, A.; A Abd-Alhameed, R. Reconfigurable

Antennas: Switching Techniques—A Survey. Electronics 2020, 9, 336. [CrossRef]

Sensors 2022, 22, 2990 34 of 36

132. Ojaroudi Parchin, N.; Jahanbakhsh Basherlou, H.; Al-Yasir, Y.I.; Abd-Alhameed, R.A.; Abdulkhaleq, A.M.; Noras, J.M. Recentdevelopments of reconfigurable antennas for current and future wireless communication systems. Electronics 2019, 8, 128.[CrossRef]

133. Yang, F.; Rahmat-Samii, Y. A reconfigurable patch antenna using switchable slots for circular polarization diversity. IEEE Microw.Wirel. Compon. Lett. 2002, 12, 96–98. [CrossRef]

134. Gour, A.; Mathur, D.; Tanwar, M. A novel CPW-fed ultra wide band T-shaped slot antenna design. Int. J. Emerg. Technol. Adv.Eng. 2011, 1, 13–16.

135. Jagadeesan, A.; Alphones, A.; Karim, M.; Ong, L. Metamaterial based reconfigurable multiband antenna. In Proceedings of the2015 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, Vancouver,BC, Canada, 19–24 July 2015; pp. 2389–2390.

136. Ahmad, W.; Budimir, D. Switchable filtennas with sharp dual bandnotch using looped resonators. In Proceedings of the 201646th European Microwave Conference (EuMC), Switchable Filtennas with Sharp Dual Bandnotch Using Looped Resonators,London, UK, 4–6 October 2016; pp. 445–448.

137. Song, C.; Huang, Y.; Zhou, J.; Carter, P. Recent advances in broadband rectennas for wireless power transfer and ambient RFenergy harvesting. In Proceedings of the 2017 11th European Conference on Antennas and Propagation (EUCAP), Paris, France,19–24 March 2017; pp. 341–345.

138. Sun, H.; Guo, Y.x.; He, M.; Zhong, Z. Design of a high-efficiency 2.45-GHz rectenna for low-input-power energy harvesting. IEEEAntennas Wirel. Propag. Lett. 2012, 11, 929–932.

139. Harouni, Z.; Cirio, L.; Osman, L.; Gharsallah, A.; Picon, O. A dual circularly polarized 2.45-GHz rectenna for wireless powertransmission. IEEE Antennas Wirel. Propag. Lett. 2011, 10, 306–309. [CrossRef]

140. Sun, H.; Guo, Y.x.; He, M.; Zhong, Z. A dual-band rectenna using broadband Yagi antenna array for ambient RF power harvesting.IEEE Antennas Wirel. Propag. Lett. 2013, 12, 918–921. [CrossRef]

141. Bakkali, A.; Pelegrí-Sebastiá, J.; Sogorb, T.; Llario, V.; Bou-Escriva, A. A dual-band antenna for RF energy harvesting systems inwireless sensor networks. J. Sens. 2016, 2016, 5725836. [CrossRef]

142. Bakkali, A.; Pelegri-Sebastia, J.; Sogorb, T.; Bou-Escriva, A.; Lyhyaoui, A. Design and simulation of dual-band RF energyharvesting antenna for WSNs. Energy Procedia 2017, 139, 55–60. [CrossRef]

143. Hebelka, V.; Velim, J.; Raida, Z. Dual band Koch antenna for RF energy harvesting. In Proceedings of the 2016 10th EuropeanConference on Antennas and Propagation (EuCAP), Davos, Switzerland, 10–15 April 2016; pp. 1–3. [CrossRef]

144. Singh, N.; Kanaujia, B.K.; Beg, M.T.; Kumar, S.; Khandelwal, M.K. A dual band rectifying antenna for RF energy harvesting. J.Comput. Electron. 2018, 17, 1748–1755. [CrossRef]

145. Jie, A.M.; Nasimuddin, N.; Karim, M.F.; Chandrasekaran, K.T. A dual-band efficient circularly polarized rectenna for RF energyharvesting systems. Int. J. Microw. Comput.-Aided Eng. 2019, 29, e21665. [CrossRef]

146. Arrawatia, M.; Baghini, M.S.; Kumar, G. Broadband bent triangular omnidirectional antenna for RF energy harvesting. IEEEAntennas Wirel. Propag. Lett. 2015, 15, 36–39. [CrossRef]

147. Song, C.; Huang, Y.; Zhou, J.; Zhang, J.; Yuan, S.; Carter, P. A high-efficiency broadband rectenna for ambient wireless energyharvesting. IEEE Trans. Antennas Propag. 2015, 63, 3486–3495. [CrossRef]

148. Wang, X..; Zhao, Z..; Chen, G..; He, F.. RF energy harvesting with broadband antenna. In Proceedings of the 2014 IEEE Conferenceand Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), Beijing, China, 31 August–3 September 2014; pp. 1–5.[CrossRef]

149. Singh, N.; Kumar, S.; Kanaujia, B.K.; Beg, M.T.; Kumar, S. A compact broadband GFET based rectenna for RF energy harvestingapplications. Microsyst. Technol. 2020, 26, 1881–1888. [CrossRef]

150. Shi, Y.; Jing, J.; Fan, Y.; Yang, L.; Li, Y.; Wang, M. A novel compact broadband rectenna for ambient RF energy harvesting. AEU-Int.J. Electron. Commun. 2018, 95, 264–270. [CrossRef]

151. Saranya, N.; Kesavamurthy, T. Design and performance analysis of broadband rectenna for an efficient RF energy harvestingapplication. Int. J. RF Microw. Comput.-Aided Eng. 2019, 29, e21628. [CrossRef]

152. Song, C.; Huang, Y.; Carter, P.; Zhou, J.; Yuan, S.; Xu, Q.; Kod, M. A novel six-band dual CP rectenna using improved impedancematching technique for ambient RF energy harvesting. IEEE Trans. Antennas Propag. 2016, 64, 3160–3171. [CrossRef]

153. Singh, N.; Kanaujia, B.K.; Beg, M.T.; Khan, T.; Kumar, S. A dual polarized multiband rectenna for RF energy harvesting. AEU-Int.J. Electron. Commun. 2018, 93, 123–131. [CrossRef]

154. Chandravanshi, S.; Sarma, S.S.; Akhtar, M.J. Design of triple band differential rectenna for RF energy harvesting. IEEE Trans.Antennas Propag. 2018, 66, 2716–2726. [CrossRef]

155. Huang, W.; Chen, H.; Li, Y.; Vucetic, B. On the Performance of Multi-antenna Wireless-Powered Communications with EnergyBeamforming. IEEE Trans. Veh. Technol. 2016, 65, 1801–1808. [CrossRef]

156. Mi, M.; Shahid, A.; Jang, J.W.; Lee, K. Energy Harvesting Non-Orthogonal Multiple Access System with Multi-Antenna Relayand Base Station. IEEE Access 2017, 5, 17660–17670. [CrossRef]

157. Huang, Y.; Wang, J.; Zhang, P.; Wu, Q. Performance Analysis of Energy Harvesting Multi-Antenna Relay Networks With DifferentAntenna Selection Schemes. IEEE Access 2018, 6, 5654–5665. [CrossRef]

158. Minhong Mi.; Mickle, M.H.; Capelli, C.; Swift, H. RF energy harvesting with multiple antennas in the same space. IEEE AntennasPropag. Mag. 2005, 47, 100–106. [CrossRef]

Sensors 2022, 22, 2990 35 of 36

159. Mrnka, M.; Vasina, P.; Kufa, M.; Hebelka, V.; Raida, Z. The RF energy harvesting antennas operating in commercially deployedfrequency bands: A comparative study. Int. J. Antennas Propag. 2016, 2016, 7379624. [CrossRef]

160. Zhao, P.; Glesner, M. RF energy harvester design with autonomously adaptive impedance matching network based on auxiliarycharge-pump rectifier. In Proceedings of the 2011 IEEE International Symposium of Circuits and Systems (ISCAS), Rio de Janeiro,Brazil, 15–18 May 2011; pp. 2477–2480.

161. Hameed, Z.; Moez, K. Design of impedance matching circuits for RF energy harvesting systems. Microelectron. J. 2017, 62, 49–56.[CrossRef]

162. Felini, C.; Merenda, M.; Della Corte, F.G. Dynamic impedance matching network for RF energy harvesting systems. In Proceedingsof the 2014 IEEE RFID Technology and Applications Conference (RFID-TA), Tampere, Finland, 8–9 September 2014; pp. 86–90.[CrossRef]

163. Agrawal, S.; Pandey, S.K.; Singh, J.; Parihar, M.S. Realization of efficient RF energy harvesting circuits employing differentmatching technique. In Proceedings of the Fifteenth International Symposium on Quality Electronic Design, Santa Clara, CA,USA, 3–5 March 2014; pp. 754–761.

164. Xia, L.; Cheng, J.; Glover, N.E.; Chiang, P. 0.56 V,–20 dBm RF-powered, multi-node wireless body area network system-on-a-chipwith harvesting-efficiency tracking loop. IEEE J. Solid-State Circuits 2014, 49, 1345–1355. [CrossRef]

165. Hameed, Z.; Moez, K. Fully-integrated passive threshold-compensated PMOS rectifier for RF energy harvesting. In Proceedingsof the 2013 IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS), Columbus, OH, USA, 4–7 August2013; pp. 129–132.

166. Oh, S.; Wentzloff, D.D. A- 32dBm sensitivity RF power harvester in 130nm CMOS. In Proceedings of the 2012 IEEE RadioFrequency Integrated Circuits Symposium, Montreal, QC, Canada, 17–19 June 2012; pp. 483–486.

167. Lu, Y.; Ki, W.H. A 13.56 MHz CMOS active rectifier with switched-offset and compensated biasing for biomedical wireless powertransfer systems. IEEE Trans. Biomed. Circuits Syst. 2013, 8, 334–344. [CrossRef] [PubMed]

168. Raben, H.; Borg, J.; Johansson, J. A Model for MOS Diodes With Cancellation in RFID Rectifiers. IEEE Trans. Circuits Syst. IIExpress Briefs 2012, 59, 761–765. [CrossRef]

169. Khansalee, E.; Zhao, Y.; Leelarasmee, E.; Nuanyai, K. A dual-band rectifier for RF energy harvesting systems. In Proceedings ofthe 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and InformationTechnology (ECTI-CON), Nakhon Ratchasima, Thailand, 14–17 May 2014; pp. 1–4.

170. ur Rehman, M.; Ahmad, W.; Khan, W.T. Highly efficient dual band 2.45/5.85 GHz rectifier for RF energy harvesting applicationsin ISM band. In Proceedings of the 2017 IEEE Asia Pacific Microwave Conference (APMC), Kuala Lumpur, Malaysia, 13–16November 2017; pp. 150–153.

171. Khansalee, E.; Nuanyai, K.; Zhao, Y. A dual-band rectifier for RF energy harvesting. Eng. J. 2015, 19, 189–197. [CrossRef]172. Tafekirt, H.; Pelegri-Sebastia, J.; Bouajaj, A.; Reda, B.M. A sensitive triple-band rectifier for energy harvesting applications. IEEE

Access 2020, 8, 73659–73664. [CrossRef]173. Liu, J.; Zhang, X.Y. Compact triple-band rectifier for ambient RF energy harvesting application. IEEE Access 2018, 6, 19018–19024.

[CrossRef]174. Sarma, S.S.; Chandravanshi, S.; Akhtar, M.J. Triple band differential rectifier for RF energy harvesting applications. In Proceedings

of the 2016 Asia-Pacific Microwave Conference (APMC), New Delhi, India, 5–9 December 2016; pp. 1–4.175. Sherazi, H.H.R.; Grieco, L.A.; Boggia, G. A comprehensive review on energy harvesting MAC protocols in WSNs: Challenges

and tradeoffs. Ad Hoc Netw. 2018, 71, 117–134. [CrossRef]176. Eu, Z.A.; Tan, H.P.; Seah, W.K. Design and performance analysis of MAC schemes for wireless sensor networks powered by

ambient energy harvesting. Ad Hoc Netw. 2011, 9, 300–323. [CrossRef]177. Fafoutis, X.; Dragoni, N. ODMAC: An on-demand MAC protocol for energy harvesting-wireless sensor networks. In Proceedings

of the 8th ACM Symposium on Performance Evaluation of Wireless ad Hoc, Sensor, and Ubiquitous Networks, Miami, FL, USA,3–4 November 2011; ACM: New York, NY, USA, 2011; pp. 49–56.

178. Yang, G.; Lin, G.Y.; Wei, H.Y. Markov chain performance model for IEEE 802.11 devices with energy harvesting source. InProceedings of the 2012 IEEE Global Communications Conference (GLOBECOM), Anaheim, CA, USA, 3–7 December 2012;pp. 5212–5217.

179. Kim, J.; Lee, J.W. Energy adaptive MAC protocol for wireless sensor networks with RF energy transfer. In Proceedings of the2011 Third International Conference on Ubiquitous and Future Networks (ICUFN), Dalian, China, 15–17 June 2011; pp. 89–94.

180. Kim, J.; Lee, J.W. Energy adaptive MAC for wireless sensor networks with RF energy transfer: Algorithm, analysis, andimplementation. Telecommun. Syst. 2017, 64, 293–307. [CrossRef]

181. Naderi, M.Y.; Nintanavongsa, P.; Chowdhury, K.R. RF-MAC: A medium access control protocol for re-chargeable sensor networkspowered by wireless energy harvesting. IEEE Trans. Wirel. Commun. 2014, 13, 3926–3937. [CrossRef]

182. Nguyen, T.D.; Khan, J.Y.; Ngo, D.T. An adaptive MAC protocol for RF energy harvesting wireless sensor networks. In Proceedingsof the Global Communications Conference (GLOBECOM), Washington, DC, USA, 4–8 December 2016; pp. 1–6.

183. Ha, T.; Kim, J.; Chung, J.M. HE-MAC: Harvest-then-transmit based modified EDCF MAC protocol for wireless powered sensornetworks. IEEE Trans. Wirel. Commun. 2017, 17, 3–16. [CrossRef]

184. Ju, H.; Zhang, R. Throughput Maximization in Wireless Powered Communication Networks. IEEE Trans. Wirel. Commun. 2014,13, 418–428. [CrossRef]

Sensors 2022, 22, 2990 36 of 36

185. Li, H.; Huang, C.; Zhang, P.; Cui, S.; Zhang, J. Distributed opportunistic scheduling for energy harvesting based wirelessnetworks: A two-stage probing approach. IEEE/ACM Trans. Netw. 2015, 24, 1618–1631. [CrossRef]

186. Kim, T.; Park, J.; Kim, J.; Noh, J.; Cho, S. REACH: An efficient MAC protocol for RF energy harvesting in wireless sensor network.Wirel. Commun. Mob. Comput. 2017, 2017, 6438726. [CrossRef]

187. Le, T.; Mayaram, K.; Fiez, T. Efficient far-field radio frequency energy harvesting for passively powered sensor networks. IEEE J.Solid-State Circuits 2008, 43, 1287–1302. [CrossRef]

188. Tapio, V.; Hemadeh, I.; Mourad, A.; Shojaeifard, A.; Juntti, M. Survey on reconfigurable intelligent surfaces below 10 GHz.EURASIP J. Wirel. Commun. Netw. 2021, 2021, 175. [CrossRef]

189. Li, Y.; Fu, L.; Chen, M.; Chi, K.; Zhu, Y.h. RF-based charger placement for duty cycle guarantee in battery-free sensor networks.IEEE Commun. Lett. 2015, 19, 1802–1805. [CrossRef]

190. Ejaz, W.; Kandeepan, S.; Anpalagan, A. Optimal placement and number of energy transmitters in wireless sensor networks forRF energy transfer. In Proceedings of the 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and MobileRadio Communications (PIMRC), Hong Kong, China, 30 August–2 September 2015; pp. 1238–1243.

191. Zorbas, D.; Raveneau, P.; Ghamri-Doudane, Y.; Douligeris, C. The charger positioning problem in clustered RF-power harvestingwireless sensor networks. Ad Hoc Netw. 2018, 78, 42–53. [CrossRef]

192. Jungnickel, V.; Manolakis, K.; Zirwas, W.; Panzner, B.; Braun, V.; Lossow, M.; Sternad, M.; Apelfröjd, R.; Svensson, T. The role ofsmall cells, coordinated multipoint, and massive MIMO in 5G. IEEE Commun. Mag. 2014, 52, 44–51. [CrossRef]

193. Code, S. Limits of Human Exposure to Radiofrequency Electromagnetic Energy in the Frequency Range from 3 khz to 300 ghz; HealthCanada: Ottawa, ON, Canada, 2015.

194. Wood, A.W. How dangerous are mobile phones, transmission masts, and electricity pylons? Arch. Dis. Child. 2006, 91, 361–366.[CrossRef]

195. Ahlbom, A.; Bridges, J.; De Seze, R.; Hillert, L.; Juutilainen, J.; Mattsson, M.O.; Neubauer, G.; Schüz, J.; Simko, M.; Bromen, K.Possible effects of electromagnetic fields (EMF) on human health–opinion of the scientific committee on emerging and newlyidentified health risks (SCENIHR). Toxicology 2008, 246, 248–250.

196. Hardell, L.; Sage, C. Biological effects from electromagnetic field exposure and public exposure standards. Biomed. Pharmacother.2008, 62, 104–109. [CrossRef] [PubMed]

197. Breckenkamp, J.; Berg-Beckhoff, G.; Münster, E.; Schüz, J.; Schlehofer, B.; Wahrendorf, J.; Blettner, M. Feasibility of a cohort studyon health risks caused by occupational exposure to radiofrequency electromagnetic fields. Environ. Health 2009, 8, 23. [CrossRef][PubMed]

198. Habash, R.W.; Elwood, J.M.; Krewski, D.; Lotz, W.G.; McNamee, J.P.; Prato, F.S. Recent advances in research on radiofrequencyfields and health: 2004–2007. J. Toxicol. Environ. Health Part B 2009, 12, 250–288. [CrossRef] [PubMed]